The repository GenomicBasedRegression provides a Python (recommended version 3.12) script called GenomicBasedRegression.py to perform regression-based modeling or prediction from binary (e.g., presence/absence of genes) or categorical (e.g., allele profiles) genomic data.
The scikit-learn (sklearn)-based Python workflow independently supports both modeling (i.e., training and testing) and prediction (i.e., using a pre-built model), and implements 5 feature selection methods, 19 model regressors, hyperparameter tuning, performance metric computation, feature and permutation importance analyses, prediction interval estimation, execution monitoring via progress bars, and parallel processing.
1.3.0 (December 2025)
The Python script GenomicBasedRegression.py was prepared and tested with the Python version 3.12 and Ubuntu 20.04 LTS Focal Fossa.
- pandas==2.2.2
- scipy==1.16.0
- scikit-learn==1.5.2
- catboost==1.2.8
- lightgbm==4.6.0
- xgboost==2.1.3
- numpy==1.26.4
- joblib==1.5.1
- tqdm==4.67.1
- tqdm-joblib==0.0.4
- SelectKBest (SKB): https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html
- SelectFromModel with Lasso (laSFM): https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html
- SelectFromModel with ElasticNet (enSFM): https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html
- SelectFromModel with ridge (riSFM): https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html
- SelectFromModel with Random Forest (rfSFM): https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html
- adaboost (ADA): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html
- bayesian bidge (BRI): https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html
- catboost (CAT): https://catboost.ai/docs/en/concepts/python-reference_catboostregressor
- decision tree (DT): https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html
- elasticnet (EN): https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html
- extra trees (ET): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
- gradient boosting (GB): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
- histogram-based gradient boosting (HGB): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html
- huber (HU): https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html
- k-nearest neighbors (KNN): https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
- lassa (LA): https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html
- light gradient goosting machine (LGBM): https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html
- multi-layer perceptron (MLP): https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html
- nu support vector (NSV): https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html
- polynomial (PN): https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
- random forest (RF): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
- ridge (RI): https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html
- support vector regressor (SVR): https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html
- extreme gradient boosting (XGB): https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBRFRegressor
pip3.12 install pandas==2.2.2
pip3.12 install scipy==1.15.3
pip3.12 install scikit-learn==1.5.2
pip3.12 install catboost==1.2.8
pip3.12 install lightgbm==4.6.0
pip3.12 install xgboost==2.1.3
pip3.12 install numpy==1.26.4
pip3.12 install joblib==1.5.1
pip3.12 install tqdm==4.67.1
pip3.12 install tqdm-joblib==0.0.4
docker pull nicolasradomski/genomicbasedregression:1.3.0
conda update --all
conda --version # conda 25.7.0
conda create --name env_conda_GenomicBasedRegression_1.3.0 python=3.12
conda activate env_conda_GenomicBasedRegression_1.3.0
python --version # Python 3.12.12
conda install -c conda-forge mamba=2.0.5
mamba install -c conda-forge pandas==2.2.2
mamba install -c conda-forge scipy==1.16.0
mamba install -c conda-forge scikit-learn==1.5.2
mamba install -c conda-forge catboost==1.2.8
mamba install -c conda-forge lightgbm==4.6.0
mamba install -c conda-forge xgboost==2.1.3
mamba install -c conda-forge numpy==1.26.4
mamba install -c conda-forge joblib==1.5.1
mamba install -c conda-forge tqdm==4.67.1
mamba install -c nicolasradomski tqdm-joblib=0.0.4
conda list -n env_conda_GenomicBasedRegression_1.3.0
conda deactivate # after usage
conda update --all
conda create -n env_anaconda_GenomicBasedRegression_1.3.0 -c nicolasradomski -c conda-forge -c defaults genomicbasedregression=1.3.0
conda activate env_anaconda_GenomicBasedRegression_1.3.0
conda deactivate # after usage
usage: GenomicBasedRegression.py modeling [-h] -m INPUTPATH_MUTATIONS -ph INPUTPATH_PHENOTYPES [-da {random,manual}] [-sp SPLITTING]
[-q QUANTILES] [-l LIMIT] [-fs FEATURESELECTION] [-r REGRESSOR] [-k FOLD] [-pa PARAMETERS]
[-j JOBS] [-pi] [-nr NREPEATS] [-a ALPHA] [-o OUTPUTPATH] [-x PREFIX] [-di DIGITS] [-de DEBUG] [-w] [-nc]
options:
-h, --help show this help message and exit
-m INPUTPATH_MUTATIONS, --mutations INPUTPATH_MUTATIONS
Absolute or relative input path of tab-separated values (tsv) file including profiles of mutations. First column: sample identifiers identical
to those in the input file of phenotypes and datasets (header: e.g., sample). Other columns: profiles of mutations (header: labels of
mutations). [MANDATORY]
-ph INPUTPATH_PHENOTYPES, --phenotypes INPUTPATH_PHENOTYPES
Absolute or relative input path of tab-separated values (tsv) file including profiles of phenotypes and datasets. First column: sample
identifiers identical to those in the input file of mutations (header: e.g., sample). Second column: categorical phenotype (header: e.g.,
phenotype). Third column: 'training' or 'testing' dataset (header: e.g., dataset). [MANDATORY]
-da {random,manual}, --dataset {random,manual}
Perform random (i.e., 'random') or manual (i.e., 'manual') splitting of training and testing datasets through the holdout method. [OPTIONAL,
DEFAULT: 'random']
-sp SPLITTING, --split SPLITTING
Percentage of random splitting when preparing the training dataset using the holdout method. [OPTIONAL, DEFAULT: None]
-q QUANTILES, --quantiles QUANTILES
Number of quantiles used to discretize the phenotype values when preparing the training dataset using the holdout method. [OPTIONAL, DEFAULT:
None]
-l LIMIT, --limit LIMIT
Recommended minimum number of samples in both the training and testing datasets to reliably estimate performance metrics. [OPTIONAL, DEFAULT:
10]
-fs FEATURESELECTION, --featureselection FEATURESELECTION
Acronym of the regression-compatible feature selection method to use: SelectKBest (SKB), SelectFromModel with lasso (laSFM), SelectFromModel
with elasticnet (enSFM), or SelectFromModel with ridge (riSFM), or SelectFromModel with random forest (rfSFM). These methods are suitable for
high-dimensional binary or categorical-encoded features. [OPTIONAL, DEFAULT: None]
-r REGRESSOR, --regressor REGRESSOR
Acronym of the regressor to use among adaboost (ADA), bayesian ridge (BRI), catboost (CAT), decision tree (DT), elasticnet (EN), extra trees
(ET), gradient boosting (GB), histogram-based gradient boosting (HGB), huber (HU), k-nearest neighbors (KNN), lassa (LA), light gradient
boosting machine (LGBM), multi-layer perceptron (MLP), nu support vector (NSV), polynomial (PN), ridge (RI), random forest (RF), support
vector regressor (SVR) or extreme gradient boosting (XGB). [OPTIONAL, DEFAULT: XGB]
-k FOLD, --fold FOLD Value defining k-1 groups of samples used to train against one group of validation through the repeated k-fold cross-validation method.
[OPTIONAL, DEFAULT: 5]
-pa PARAMETERS, --parameters PARAMETERS
Absolute or relative input path of a text (txt) file including tuning parameters compatible with the param_grid argument of the GridSearchCV
function. (OPTIONAL)
-j JOBS, --jobs JOBS Value defining the number of jobs to run in parallel compatible with the n_jobs argument of the GridSearchCV function. [OPTIONAL, DEFAULT: -1]
-pi, --permutationimportance
Compute permutation importance, which can be computationally expensive, especially with many features and/or high repetition counts.
[OPTIONAL, DEFAULT: False]
-nr NREPEATS, --nrepeats NREPEATS
Number of repetitions per feature for permutation importance; higher values provide more stable estimates but increase runtime. [OPTIONAL,
DEFAULT: 10]
-a ALPHA, --alpha ALPHA
Significance level alpha (a float between 0 and 1) used to compute prediction intervals corresponding to a [(1 − alpha) × 100]% coverage.
[OPTIONAL, DEFAULT: 0.05]
-o OUTPUTPATH, --output OUTPUTPATH
Output path. [OPTIONAL, DEFAULT: .]
-x PREFIX, --prefix PREFIX
Prefix of output files. [OPTIONAL, DEFAULT: output]
-di DIGITS, --digits DIGITS
Number of decimal digits to round numerical results (e.g., root mean squared error, importance, metrics). [OPTIONAL, DEFAULT: 6]
-de DEBUG, --debug DEBUG
Traceback level when an error occurs. [OPTIONAL, DEFAULT: 0]
-w, --warnings Do not ignore warnings if you want to improve the script. [OPTIONAL, DEFAULT: False]
-nc, --no-check Do not check versions of Python and packages. [OPTIONAL, DEFAULT: False]
usage: GenomicBasedRegression.py prediction [-h] -m INPUTPATH_MUTATIONS -f INPUTPATH_FEATURES -fe INPUTPATH_FEATURE_ENCODER
-cf INPUTPATH_CALIBRATION_FEATURES -ct INPUTPATH_CALIBRATION_TARGETS -t INPUTPATH_MODEL
[-a ALPHA] [-o OUTPUTPATH] [-x PREFIX] [-di DIGITS] [-de DEBUG] [-w] [-nc]
options:
-h, --help show this help message and exit
-m INPUTPATH_MUTATIONS, --mutations INPUTPATH_MUTATIONS
Absolute or relative input path of a tab-separated values (tsv) file including profiles of mutations. First column: sample identifiers
identical to those in the input file of phenotypes and datasets (header: e.g., sample). Other columns: profiles of mutations (header: labels
of mutations). [MANDATORY]
-f INPUTPATH_FEATURES, --features INPUTPATH_FEATURES
Absolute or relative input path of an object (obj) file including features from the training dataset (i.e., mutations). [MANDATORY]
-fe INPUTPATH_FEATURE_ENCODER, --featureencoder INPUTPATH_FEATURE_ENCODER
Absolute or relative input path of an object (obj) file including encoder from the training dataset (i.e., mutations). [MANDATORY]
-cf INPUTPATH_CALIBRATION_FEATURES, --calibrationfeatures INPUTPATH_CALIBRATION_FEATURES
Absolute or relative input path of an object (obj) file including calibration features from the training dataset (i.e., mutations).
[MANDATORY]
-ct INPUTPATH_CALIBRATION_TARGETS, --calibrationtargets INPUTPATH_CALIBRATION_TARGETS
Absolute or relative input path of an object (obj) file including calibration targets from the training dataset (i.e., mutations). [MANDATORY]
-t INPUTPATH_MODEL, --model INPUTPATH_MODEL
Absolute or relative input path of an object (obj) file including a trained scikit-learn model. [MANDATORY]
-a ALPHA, --alpha ALPHA
Significance level alpha (a float between 0 and 1) used to compute prediction intervals corresponding to a [(1 − alpha) × 100]% coverage.
[OPTIONAL, DEFAULT: 0.05]
-o OUTPUTPATH, --output OUTPUTPATH
Absolute or relative output path. [OPTIONAL, DEFAULT: .]
-x PREFIX, --prefix PREFIX
Prefix of output files. [OPTIONAL, DEFAULT: output_]
-di DIGITS, --digits DIGITS
Number of decimal digits to round numerical results (e.g., root mean squared error, importance, metrics). [OPTIONAL, DEFAULT: 6]
-de DEBUG, --debug DEBUG
Traceback level when an error occurs. [OPTIONAL, DEFAULT: 0]
-w, --warnings Do not ignore warnings if you want to improve the script. [OPTIONAL, DEFAULT: False]
-nc, --no-check Do not check versions of Python and packages. [OPTIONAL, DEFAULT: False]
sample phenotype dataset
S0.1.01 20 training
S0.1.02 20 training
S0.1.03 15 training
S0.1.04 48 training
S0.1.05 14 testing
S0.1.06 16 training
S0.1.07 47 training
S0.1.08 17 training
S0.1.09 58 training
S0.1.10 46 testing
genomic data for modeling (e.g., genomic_profiles_for_modeling.tsv). "A" and "L" stand for alleles and locus, respectively
sample L_1 L_2 L_3 L_4 L_5 L_6 L_7 L_8 L_9 L_10
S0.1.01 A3 A2 A3 A4 A5 A6 A7 A3 A4 A10
S0.1.02 A8 A5 A3 A4 A5 A6 A7 A3 A4 A10
S0.1.03 A6 A7 A6 A2 A17 A5 A6 A7 A8 A18
S0.1.04 A12 A44 A8 A5 A16 A4 A5 A6 A12 A17
S0.1.05 A6 A7 A15 A16 A3 A14 A6 A7 A8 A18
S0.1.06 A6 A7 A15 A16 A8 A5 A6 A7 A8 A18
S0.1.07 A7 A9 A10 A11 A14 A3 A2 A10 A16
S0.1.08 A6 A7 A15 A16 A17 A5 A7 A5 A8 A18
S0.1.09 A12 A13 A14 A15 A16 A4 A5 A6 A3 A2
S0.1.10 A12 A13 A14 A15 A16 A4 A5 A6 A8 A8
example with the SKB feature selection method and the XGB model regressor (tuning_parameters_XGB.txt)
{
# --- Feature selection (SelectKBest) ---
# used only to modify selected SKB behavior
'feature_selection__k': [25, 50], # number of top features to keep
'feature_selection__score_func': [mutual_info_regression], # score function (avoid chi2 in a context of regression and f_regression it captures only linear dependancies)
# --- Model tuning (XGBRegressor) ---
# used only to modify XGBRegressor behavior
# simplified tuning used together with SKB
'model__max_depth': [3, 4, 5], # shallow to moderate tree depth for speed and flexibility
'model__learning_rate': [0.05, 0.1, 0.2], # slower to moderate learning rates for stable training
'model__subsample': [0.7, 0.8], # moderate data subsampling to reduce overfitting
'model__colsample_bytree': [0.7], # balanced feature subsampling
'model__n_estimators': [50], # fewer boosting rounds for speed
'model__gamma': [0], # no complexity penalty by default
'model__importance_type': ['gain'] # importance extraction
}
- tuning_parameters_ADA.txt
- tuning_parameters_BRI.txt
- tuning_parameters_CAT.txt
- tuning_parameters_DT.txt
- tuning_parameters_EN.txt
- tuning_parameters_ET.txt
- tuning_parameters_GB.txt
- tuning_parameters_HGB.txt
- tuning_parameters_HU.txt
- tuning_parameters_KNN.txt
- tuning_parameters_LA.txt
- tuning_parameters_LGBM.txt
- tuning_parameters_MLP.txt
- tuning_parameters_NSV.txt
- tuning_parameters_PN.txt
- tuning_parameters_RF.txt
- tuning_parameters_RI.txt
- tuning_parameters_SVR.txt
- tuning_parameters_XGB.txt
genomic profils for prediction (e.g., genomic_profiles_for_prediction.tsv). "A" and "L" stand for alleles and locus, respectively
sample L_1 L_2 L_3 L_4 L_5 L_6 L_7 L_8 L_9 L_10 L_11
S2.1.01 A3 A2 A3 A4 A5 A6 A7 A3 A4 A1 A10
S2.1.02 A8 A5 A3 A4 A5 A6 A7 A3 A4 A1 A10
S2.1.03 A6 A7 A6 A2 A17 A5 A6 A7 A8 A1 A18
S2.1.04 A13 A8 A5 A16 A4 A5 A6 A12 A1 A17
S2.1.05 A6 A24 A15 A16 A3 A14 A6 A7 A8 A1 A18
S2.1.06 A6 A7 A15 A16 A8 A5 A6 A7 A8 A1 A18
S2.1.07 A7 A8 A9 A10 A11 A14 A3 A2 A88 A1 A16
S2.1.08 A6 A7 A15 A16 A17 A5 A7 A5 A8 A1 A18
S2.1.09 A12 A13 A14 A25 A16 A4 A5 A3 A1 A2
S2.1.10 A12 A13 A14 A15 A16 A4 A5 A6 A8 A1 A8
git clone --branch v1.3.0 --single-branch https://github.com/Nicolas-Radomski/GenomicBasedRegression.git
cd GenomicBasedRegression
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph phenotype_dataset.tsv -o MyDirectory -x ADA_FirstAnalysis -da random -sp 80 -q 10 -r ADA -k 5 -pa tuning_parameters_ADA.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/ADA_FirstAnalysis_features.obj -fe MyDirectory/ADA_FirstAnalysis_feature_encoder.obj -cf MyDirectory/ADA_FirstAnalysis_calibration_features.obj -ct MyDirectory/ADA_FirstAnalysis_calibration_targets.obj -t MyDirectory/ADA_FirstAnalysis_model.obj -o MyDirectory -x ADA_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x BRI_FirstAnalysis -da manual -fs SKB -r BRI -k 5 -pa tuning_parameters_BRI.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/BRI_FirstAnalysis_features.obj -fe MyDirectory/BRI_FirstAnalysis_feature_encoder.obj -cf MyDirectory/BRI_FirstAnalysis_calibration_features.obj -ct MyDirectory/BRI_FirstAnalysis_calibration_targets.obj -t MyDirectory/BRI_FirstAnalysis_model.obj -o MyDirectory -x BRI_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x CAT_FirstAnalysis -da manual -fs laSFM -r CAT -k 5 -pa tuning_parameters_CAT.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/CAT_FirstAnalysis_features.obj -fe MyDirectory/CAT_FirstAnalysis_feature_encoder.obj -cf MyDirectory/CAT_FirstAnalysis_calibration_features.obj -ct MyDirectory/CAT_FirstAnalysis_calibration_targets.obj -t MyDirectory/CAT_FirstAnalysis_model.obj -o MyDirectory -x CAT_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x DT_FirstAnalysis -da manual -fs enSFM -r DT -k 5 -pa tuning_parameters_DT.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/DT_FirstAnalysis_features.obj -fe MyDirectory/DT_FirstAnalysis_feature_encoder.obj -cf MyDirectory/DT_FirstAnalysis_calibration_features.obj -ct MyDirectory/DT_FirstAnalysis_calibration_targets.obj -t MyDirectory/DT_FirstAnalysis_model.obj -o MyDirectory -x DT_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x EN_FirstAnalysis -da manual -fs riSFM -r EN -k 5 -pa tuning_parameters_EN.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/EN_FirstAnalysis_features.obj -fe MyDirectory/EN_FirstAnalysis_feature_encoder.obj -cf MyDirectory/EN_FirstAnalysis_calibration_features.obj -ct MyDirectory/EN_FirstAnalysis_calibration_targets.obj -t MyDirectory/EN_FirstAnalysis_model.obj -o MyDirectory -x EN_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x ET_FirstAnalysis -da manual -fs rfSFM -r ET -k 5 -pa tuning_parameters_ET.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/ET_FirstAnalysis_features.obj -fe MyDirectory/ET_FirstAnalysis_feature_encoder.obj -cf MyDirectory/ET_FirstAnalysis_calibration_features.obj -ct MyDirectory/ET_FirstAnalysis_calibration_targets.obj -t MyDirectory/ET_FirstAnalysis_model.obj -o MyDirectory -x ET_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x GB_FirstAnalysis -da manual -fs SKB -r GB -k 5 -pa tuning_parameters_GB.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/GB_FirstAnalysis_features.obj -fe MyDirectory/GB_FirstAnalysis_feature_encoder.obj -cf MyDirectory/GB_FirstAnalysis_calibration_features.obj -ct MyDirectory/GB_FirstAnalysis_calibration_targets.obj -t MyDirectory/GB_FirstAnalysis_model.obj -o MyDirectory -x GB_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x HGB_FirstAnalysis -da manual -fs SKB -r HGB -k 5 -pa tuning_parameters_HGB.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/HGB_FirstAnalysis_features.obj -fe MyDirectory/HGB_FirstAnalysis_feature_encoder.obj -cf MyDirectory/HGB_FirstAnalysis_calibration_features.obj -ct MyDirectory/HGB_FirstAnalysis_calibration_targets.obj -t MyDirectory/HGB_FirstAnalysis_model.obj -o MyDirectory -x HGB_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x HU_FirstAnalysis -da manual -fs SKB -r HU -k 5 -pa tuning_parameters_HU.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/HU_FirstAnalysis_features.obj -fe MyDirectory/HU_FirstAnalysis_feature_encoder.obj -cf MyDirectory/HU_FirstAnalysis_calibration_features.obj -ct MyDirectory/HU_FirstAnalysis_calibration_targets.obj -t MyDirectory/HU_FirstAnalysis_model.obj -o MyDirectory -x HU_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x KNN_FirstAnalysis -da manual -fs SKB -r KNN -k 5 -pa tuning_parameters_KNN.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/KNN_FirstAnalysis_features.obj -fe MyDirectory/KNN_FirstAnalysis_feature_encoder.obj -cf MyDirectory/KNN_FirstAnalysis_calibration_features.obj -ct MyDirectory/KNN_FirstAnalysis_calibration_targets.obj -t MyDirectory/KNN_FirstAnalysis_model.obj -o MyDirectory -x KNN_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x LA_FirstAnalysis -da manual -fs SKB -r LA -k 5 -pa tuning_parameters_LA.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/LA_FirstAnalysis_features.obj -fe MyDirectory/LA_FirstAnalysis_feature_encoder.obj -cf MyDirectory/LA_FirstAnalysis_calibration_features.obj -ct MyDirectory/LA_FirstAnalysis_calibration_targets.obj -t MyDirectory/LA_FirstAnalysis_model.obj -o MyDirectory -x LA_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x LGBM_FirstAnalysis -da manual -fs SKB -r LGBM -k 5 -pa tuning_parameters_LGBM.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/LGBM_FirstAnalysis_features.obj -fe MyDirectory/LGBM_FirstAnalysis_feature_encoder.obj -cf MyDirectory/LGBM_FirstAnalysis_calibration_features.obj -ct MyDirectory/LGBM_FirstAnalysis_calibration_targets.obj -t MyDirectory/LGBM_FirstAnalysis_model.obj -o MyDirectory -x LGBM_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x MLP_FirstAnalysis -da manual -fs SKB -r MLP -k 5 -pa tuning_parameters_MLP.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/MLP_FirstAnalysis_features.obj -fe MyDirectory/MLP_FirstAnalysis_feature_encoder.obj -cf MyDirectory/MLP_FirstAnalysis_calibration_features.obj -ct MyDirectory/MLP_FirstAnalysis_calibration_targets.obj -t MyDirectory/MLP_FirstAnalysis_model.obj -o MyDirectory -x MLP_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x NSV_FirstAnalysis -da manual -fs SKB -r NSV -k 5 -pa tuning_parameters_NSV.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/NSV_FirstAnalysis_features.obj -fe MyDirectory/NSV_FirstAnalysis_feature_encoder.obj -cf MyDirectory/NSV_FirstAnalysis_calibration_features.obj -ct MyDirectory/NSV_FirstAnalysis_calibration_targets.obj -t MyDirectory/NSV_FirstAnalysis_model.obj -o MyDirectory -x NSV_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x PN_FirstAnalysis -da manual -fs SKB -r PN -k 5 -pa tuning_parameters_PN.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/PN_FirstAnalysis_features.obj -fe MyDirectory/PN_FirstAnalysis_feature_encoder.obj -cf MyDirectory/PN_FirstAnalysis_calibration_features.obj -ct MyDirectory/PN_FirstAnalysis_calibration_targets.obj -t MyDirectory/PN_FirstAnalysis_model.obj -o MyDirectory -x PN_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x RF_FirstAnalysis -da manual -fs SKB -r RF -k 5 -pa tuning_parameters_RF.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/RF_FirstAnalysis_features.obj -fe MyDirectory/RF_FirstAnalysis_feature_encoder.obj -cf MyDirectory/RF_FirstAnalysis_calibration_features.obj -ct MyDirectory/RF_FirstAnalysis_calibration_targets.obj -t MyDirectory/RF_FirstAnalysis_model.obj -o MyDirectory -x RF_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x RI_FirstAnalysis -da manual -fs SKB -r RI -k 5 -pa tuning_parameters_RI.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/RI_FirstAnalysis_features.obj -fe MyDirectory/RI_FirstAnalysis_feature_encoder.obj -cf MyDirectory/RI_FirstAnalysis_calibration_features.obj -ct MyDirectory/RI_FirstAnalysis_calibration_targets.obj -t MyDirectory/RI_FirstAnalysis_model.obj -o MyDirectory -x RI_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x SVR_FirstAnalysis -da manual -fs SKB -r SVR -k 5 -pa tuning_parameters_SVR.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/SVR_FirstAnalysis_features.obj -fe MyDirectory/SVR_FirstAnalysis_feature_encoder.obj -cf MyDirectory/SVR_FirstAnalysis_calibration_features.obj -ct MyDirectory/SVR_FirstAnalysis_calibration_targets.obj -t MyDirectory/SVR_FirstAnalysis_model.obj -o MyDirectory -x SVR_SecondAnalysis
python3.12 GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectory/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectory -x XGB_FirstAnalysis -da manual -fs SKB -r XGB -k 5 -pa tuning_parameters_XGB.txt -pi
python3.12 GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectory/XGB_FirstAnalysis_features.obj -fe MyDirectory/XGB_FirstAnalysis_feature_encoder.obj -cf MyDirectory/XGB_FirstAnalysis_calibration_features.obj -ct MyDirectory/XGB_FirstAnalysis_calibration_targets.obj -t MyDirectory/XGB_FirstAnalysis_model.obj -o MyDirectory -x XGB_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph phenotype_dataset.tsv -o MyDirectoryDockerHub -x ADA_FirstAnalysis -da random -sp 80 -q 10 -r ADA -k 5 -pa tuning_parameters_ADA.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/ADA_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/ADA_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/ADA_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/ADA_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/ADA_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x ADA_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x BRI_FirstAnalysis -da manual -fs SKB -r BRI -k 5 -pa tuning_parameters_BRI.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/BRI_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/BRI_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/BRI_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/BRI_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/BRI_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x BRI_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x CAT_FirstAnalysis -da manual -fs laSFM -r CAT -k 5 -pa tuning_parameters_CAT.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/CAT_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/CAT_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/CAT_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/CAT_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/CAT_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x CAT_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x DT_FirstAnalysis -da manual -fs enSFM -r DT -k 5 -pa tuning_parameters_DT.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/DT_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/DT_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/DT_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/DT_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/DT_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x DT_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x EN_FirstAnalysis -da manual -fs riSFM -r EN -k 5 -pa tuning_parameters_EN.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/EN_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/EN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/EN_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/EN_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/EN_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x EN_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x ET_FirstAnalysis -da manual -fs rfSFM -r ET -k 5 -pa tuning_parameters_ET.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/ET_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/ET_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/ET_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/ET_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/ET_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x ET_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x GB_FirstAnalysis -da manual -fs SKB -r GB -k 5 -pa tuning_parameters_GB.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/GB_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/GB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/GB_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/GB_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/GB_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x GB_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x HGB_FirstAnalysis -da manual -fs SKB -r HGB -k 5 -pa tuning_parameters_HGB.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/HGB_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/HGB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/HGB_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/HGB_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/HGB_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x HGB_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x HU_FirstAnalysis -da manual -fs SKB -r HU -k 5 -pa tuning_parameters_HU.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/HU_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/HU_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/HU_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/HU_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/HU_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x HU_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x KNN_FirstAnalysis -da manual -fs SKB -r KNN -k 5 -pa tuning_parameters_KNN.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/KNN_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/KNN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/KNN_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/KNN_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/KNN_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x KNN_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x LA_FirstAnalysis -da manual -fs SKB -r LA -k 5 -pa tuning_parameters_LA.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/LA_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/LA_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/LA_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/LA_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/LA_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x LA_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x LGBM_FirstAnalysis -da manual -fs SKB -r LGBM -k 5 -pa tuning_parameters_LGBM.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/LGBM_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/LGBM_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/LGBM_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/LGBM_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/LGBM_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x LGBM_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x MLP_FirstAnalysis -da manual -fs SKB -r MLP -k 5 -pa tuning_parameters_MLP.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/MLP_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/MLP_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/MLP_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/MLP_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/MLP_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x MLP_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x NSV_FirstAnalysis -da manual -fs SKB -r NSV -k 5 -pa tuning_parameters_NSV.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/NSV_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/NSV_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/NSV_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/NSV_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/NSV_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x NSV_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x PN_FirstAnalysis -da manual -fs SKB -r PN -k 5 -pa tuning_parameters_PN.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/PN_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/PN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/PN_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/PN_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/PN_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x PN_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x RF_FirstAnalysis -da manual -fs SKB -r RF -k 5 -pa tuning_parameters_RF.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/RF_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/RF_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/RF_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/RF_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/RF_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x RF_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x RI_FirstAnalysis -da manual -fs SKB -r RI -k 5 -pa tuning_parameters_RI.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/RI_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/RI_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/RI_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/RI_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/RI_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x RI_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x SVR_FirstAnalysis -da manual -fs SKB -r SVR -k 5 -pa tuning_parameters_SVR.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/SVR_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/SVR_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/SVR_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/SVR_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/SVR_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x SVR_SecondAnalysis
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryDockerHub/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryDockerHub -x XGB_FirstAnalysis -da manual -fs SKB -r XGB -k 5 -pa tuning_parameters_XGB.txt -pi
docker run --rm --name nicolas -u $(id -u):$(id -g) -v $(pwd):/wd nicolasradomski/genomicbasedregression:1.3.0 prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryDockerHub/XGB_FirstAnalysis_features.obj -fe MyDirectoryDockerHub/XGB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryDockerHub/XGB_FirstAnalysis_calibration_features.obj -ct MyDirectoryDockerHub/XGB_FirstAnalysis_calibration_targets.obj -t MyDirectoryDockerHub/XGB_FirstAnalysis_model.obj -o MyDirectoryDockerHub -x XGB_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph phenotype_dataset.tsv -o MyDirectoryConda -x ADA_FirstAnalysis -da random -sp 80 -q 10 -r ADA -k 5 -pa tuning_parameters_ADA.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/ADA_FirstAnalysis_features.obj -fe MyDirectoryConda/ADA_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/ADA_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/ADA_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/ADA_FirstAnalysis_model.obj -o MyDirectoryConda -x ADA_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x BRI_FirstAnalysis -da manual -fs SKB -r BRI -k 5 -pa tuning_parameters_BRI.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/BRI_FirstAnalysis_features.obj -fe MyDirectoryConda/BRI_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/BRI_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/BRI_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/BRI_FirstAnalysis_model.obj -o MyDirectoryConda -x BRI_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x CAT_FirstAnalysis -da manual -fs laSFM -r CAT -k 5 -pa tuning_parameters_CAT.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/CAT_FirstAnalysis_features.obj -fe MyDirectoryConda/CAT_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/CAT_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/CAT_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/CAT_FirstAnalysis_model.obj -o MyDirectoryConda -x CAT_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x DT_FirstAnalysis -da manual -fs enSFM -r DT -k 5 -pa tuning_parameters_DT.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/DT_FirstAnalysis_features.obj -fe MyDirectoryConda/DT_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/DT_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/DT_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/DT_FirstAnalysis_model.obj -o MyDirectoryConda -x DT_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x EN_FirstAnalysis -da manual -fs riSFM -r EN -k 5 -pa tuning_parameters_EN.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/EN_FirstAnalysis_features.obj -fe MyDirectoryConda/EN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/EN_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/EN_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/EN_FirstAnalysis_model.obj -o MyDirectoryConda -x EN_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x ET_FirstAnalysis -da manual -fs rfSFM -r ET -k 5 -pa tuning_parameters_ET.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/ET_FirstAnalysis_features.obj -fe MyDirectoryConda/ET_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/ET_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/ET_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/ET_FirstAnalysis_model.obj -o MyDirectoryConda -x ET_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x GB_FirstAnalysis -da manual -fs SKB -r GB -k 5 -pa tuning_parameters_GB.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/GB_FirstAnalysis_features.obj -fe MyDirectoryConda/GB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/GB_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/GB_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/GB_FirstAnalysis_model.obj -o MyDirectoryConda -x GB_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x HGB_FirstAnalysis -da manual -fs SKB -r HGB -k 5 -pa tuning_parameters_HGB.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/HGB_FirstAnalysis_features.obj -fe MyDirectoryConda/HGB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/HGB_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/HGB_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/HGB_FirstAnalysis_model.obj -o MyDirectoryConda -x HGB_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x HU_FirstAnalysis -da manual -fs SKB -r HU -k 5 -pa tuning_parameters_HU.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/HU_FirstAnalysis_features.obj -fe MyDirectoryConda/HU_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/HU_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/HU_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/HU_FirstAnalysis_model.obj -o MyDirectoryConda -x HU_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x KNN_FirstAnalysis -da manual -fs SKB -r KNN -k 5 -pa tuning_parameters_KNN.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/KNN_FirstAnalysis_features.obj -fe MyDirectoryConda/KNN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/KNN_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/KNN_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/KNN_FirstAnalysis_model.obj -o MyDirectoryConda -x KNN_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x LA_FirstAnalysis -da manual -fs SKB -r LA -k 5 -pa tuning_parameters_LA.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/LA_FirstAnalysis_features.obj -fe MyDirectoryConda/LA_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/LA_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/LA_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/LA_FirstAnalysis_model.obj -o MyDirectoryConda -x LA_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x LGBM_FirstAnalysis -da manual -fs SKB -r LGBM -k 5 -pa tuning_parameters_LGBM.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/LGBM_FirstAnalysis_features.obj -fe MyDirectoryConda/LGBM_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/LGBM_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/LGBM_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/LGBM_FirstAnalysis_model.obj -o MyDirectoryConda -x LGBM_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x MLP_FirstAnalysis -da manual -fs SKB -r MLP -k 5 -pa tuning_parameters_MLP.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/MLP_FirstAnalysis_features.obj -fe MyDirectoryConda/MLP_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/MLP_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/MLP_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/MLP_FirstAnalysis_model.obj -o MyDirectoryConda -x MLP_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x NSV_FirstAnalysis -da manual -fs SKB -r NSV -k 5 -pa tuning_parameters_NSV.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/NSV_FirstAnalysis_features.obj -fe MyDirectoryConda/NSV_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/NSV_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/NSV_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/NSV_FirstAnalysis_model.obj -o MyDirectoryConda -x NSV_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x PN_FirstAnalysis -da manual -fs SKB -r PN -k 5 -pa tuning_parameters_PN.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/PN_FirstAnalysis_features.obj -fe MyDirectoryConda/PN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/PN_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/PN_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/PN_FirstAnalysis_model.obj -o MyDirectoryConda -x PN_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x RF_FirstAnalysis -da manual -fs SKB -r RF -k 5 -pa tuning_parameters_RF.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/RF_FirstAnalysis_features.obj -fe MyDirectoryConda/RF_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/RF_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/RF_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/RF_FirstAnalysis_model.obj -o MyDirectoryConda -x RF_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x RI_FirstAnalysis -da manual -fs SKB -r RI -k 5 -pa tuning_parameters_RI.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/RI_FirstAnalysis_features.obj -fe MyDirectoryConda/RI_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/RI_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/RI_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/RI_FirstAnalysis_model.obj -o MyDirectoryConda -x RI_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x SVR_FirstAnalysis -da manual -fs SKB -r SVR -k 5 -pa tuning_parameters_SVR.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/SVR_FirstAnalysis_features.obj -fe MyDirectoryConda/SVR_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/SVR_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/SVR_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/SVR_FirstAnalysis_model.obj -o MyDirectoryConda -x SVR_SecondAnalysis
python GenomicBasedRegression.py modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryConda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryConda -x XGB_FirstAnalysis -da manual -fs SKB -r XGB -k 5 -pa tuning_parameters_XGB.txt -pi
python GenomicBasedRegression.py prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryConda/XGB_FirstAnalysis_features.obj -fe MyDirectoryConda/XGB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryConda/XGB_FirstAnalysis_calibration_features.obj -ct MyDirectoryConda/XGB_FirstAnalysis_calibration_targets.obj -t MyDirectoryConda/XGB_FirstAnalysis_model.obj -o MyDirectoryConda -x XGB_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph phenotype_dataset.tsv -o MyDirectoryAnaconda -x ADA_FirstAnalysis -da random -sp 80 -q 10 -r ADA -k 5 -pa tuning_parameters_ADA.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/ADA_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/ADA_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/ADA_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/ADA_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/ADA_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x ADA_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x BRI_FirstAnalysis -da manual -fs SKB -r BRI -k 5 -pa tuning_parameters_BRI.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/BRI_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/BRI_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/BRI_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/BRI_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/BRI_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x BRI_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x CAT_FirstAnalysis -da manual -fs laSFM -r CAT -k 5 -pa tuning_parameters_CAT.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/CAT_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/CAT_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/CAT_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/CAT_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/CAT_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x CAT_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x DT_FirstAnalysis -da manual -fs enSFM -r DT -k 5 -pa tuning_parameters_DT.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/DT_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/DT_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/DT_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/DT_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/DT_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x DT_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x EN_FirstAnalysis -da manual -fs riSFM -r EN -k 5 -pa tuning_parameters_EN.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/EN_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/EN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/EN_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/EN_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/EN_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x EN_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x ET_FirstAnalysis -da manual -fs rfSFM -r ET -k 5 -pa tuning_parameters_ET.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/ET_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/ET_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/ET_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/ET_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/ET_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x ET_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x GB_FirstAnalysis -da manual -fs SKB -r GB -k 5 -pa tuning_parameters_GB.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/GB_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/GB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/GB_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/GB_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/GB_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x GB_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x HGB_FirstAnalysis -da manual -fs SKB -r HGB -k 5 -pa tuning_parameters_HGB.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/HGB_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/HGB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/HGB_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/HGB_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/HGB_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x HGB_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x HU_FirstAnalysis -da manual -fs SKB -r HU -k 5 -pa tuning_parameters_HU.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/HU_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/HU_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/HU_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/HU_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/HU_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x HU_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x KNN_FirstAnalysis -da manual -fs SKB -r KNN -k 5 -pa tuning_parameters_KNN.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/KNN_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/KNN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/KNN_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/KNN_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/KNN_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x KNN_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x LA_FirstAnalysis -da manual -fs SKB -r LA -k 5 -pa tuning_parameters_LA.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/LA_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/LA_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/LA_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/LA_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/LA_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x LA_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x LGBM_FirstAnalysis -da manual -fs SKB -r LGBM -k 5 -pa tuning_parameters_LGBM.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/LGBM_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/LGBM_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/LGBM_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/LGBM_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/LGBM_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x LGBM_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x MLP_FirstAnalysis -da manual -fs SKB -r MLP -k 5 -pa tuning_parameters_MLP.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/MLP_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/MLP_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/MLP_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/MLP_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/MLP_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x MLP_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x NSV_FirstAnalysis -da manual -fs SKB -r NSV -k 5 -pa tuning_parameters_NSV.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/NSV_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/NSV_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/NSV_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/NSV_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/NSV_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x NSV_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x PN_FirstAnalysis -da manual -fs SKB -r PN -k 5 -pa tuning_parameters_PN.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/PN_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/PN_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/PN_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/PN_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/PN_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x PN_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x RF_FirstAnalysis -da manual -fs SKB -r RF -k 5 -pa tuning_parameters_RF.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/RF_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/RF_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/RF_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/RF_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/RF_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x RF_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x RI_FirstAnalysis -da manual -fs SKB -r RI -k 5 -pa tuning_parameters_RI.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/RI_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/RI_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/RI_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/RI_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/RI_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x RI_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x SVR_FirstAnalysis -da manual -fs SKB -r SVR -k 5 -pa tuning_parameters_SVR.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/SVR_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/SVR_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/SVR_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/SVR_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/SVR_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x SVR_SecondAnalysis
genomicbasedregression modeling -m genomic_profils_for_modeling.tsv -ph MyDirectoryAnaconda/ADA_FirstAnalysis_phenotype_dataset.tsv -o MyDirectoryAnaconda -x XGB_FirstAnalysis -da manual -fs SKB -r XGB -k 5 -pa tuning_parameters_XGB.txt -pi
genomicbasedregression prediction -m genomic_profils_for_prediction.tsv -f MyDirectoryAnaconda/XGB_FirstAnalysis_features.obj -fe MyDirectoryAnaconda/XGB_FirstAnalysis_feature_encoder.obj -cf MyDirectoryAnaconda/XGB_FirstAnalysis_calibration_features.obj -ct MyDirectoryAnaconda/XGB_FirstAnalysis_calibration_targets.obj -t MyDirectoryAnaconda/XGB_FirstAnalysis_model.obj -o MyDirectoryAnaconda -x XGB_SecondAnalysis
feature importance
Locus_5_A16 4956.503906
Locus_2_A13 3447.952393
Locus_1_A12 2031.842773
Locus_9_A4 799.916992
Locus_1_A7 228.568069
Locus_6_A9 203.670471
Locus_1_A4 194.304108
Locus_9_A9 187.599777
Locus_10_A18 126.711555
Locus_4_A4 83.494476
Locus_1_A6 76.759834
Locus_6_A6 58.775444
Locus_6_A5 51.835064
Locus_7_A5 41.836422
Locus_7_A7 36.828129
Locus_3_A8 29.107296
Locus_3_A3 20.935595
Locus_9_A8 15.511682
Locus_8_A13 15.191111
Locus_4_A15 12.305605
feature train_mean train_std test_mean test_std
Locus_3_A8 8.481091 0.949153 8.722864 0.918224
Locus_5_A16 4.354647 0.506638 5.663910 1.070395
Locus_6_A5 1.940999 0.321063 2.267965 0.272242
Locus_4_A16 1.442172 0.408884 1.538514 0.555739
Locus_1_A7 0.443297 0.072246 0.933021 0.134982
Locus_4_A4 0.057576 0.014683 0.106935 0.065868
Locus_7_A5 0.000076 0.000766 -0.000918 0.000655
Locus_9_A8 0.000000 0.000000 0.000000 0.000000
Locus_9_A4 0.000000 0.000000 0.000000 0.000000
Locus_8_A3 0.000000 0.000000 0.000000 0.000000
Locus_8_A13 0.000000 0.000000 0.000000 0.000000
Locus_7_A7 0.000000 0.000000 0.000000 0.000000
Locus_6_A9 0.000000 0.000000 0.000000 0.000000
Locus_6_A6 0.000000 0.000000 0.000000 0.000000
Locus_10_A18 0.000000 0.000000 0.000000 0.000000
Locus_6_A4 0.000000 0.000000 0.000000 0.000000
Locus_1_A12 0.000000 0.000000 0.000000 0.000000
Locus_4_A15 0.000000 0.000000 0.000000 0.000000
Locus_3_A6 0.000000 0.000000 0.000000 0.000000
Locus_3_A3 0.000000 0.000000 0.000000 0.000000
from the training dataset:
RMSE MSE SMAPE MAPE MAE R2 aR2
2.918598 8.518213 0.148581 0.167555 1.697231 0.972533 0.97069
from the testing dataset:
RMSE MSE SMAPE MAPE MAE R2 aR2
1.670332 2.790008 0.098399 0.108888 1.199855 0.991387 0.988417
sample expectation prediction lower upper
S0.1.01 20.0 25.908098 16.967949 32.375763
S0.1.03 15.0 16.706976 9.052378 24.460192
S0.1.04 48.0 48.135574 40.286213 55.694027
S0.1.06 16.0 15.921209 8.255709 23.663523
S0.1.10 46.0 46.134380 38.543644 53.951458
S0.1.11 15.0 15.921209 8.255709 23.663523
S0.1.13 13.0 14.855770 7.429060 22.836874
S0.1.14 5.0 4.810639 -2.872292 12.535522
S0.1.15 6.0 6.737475 -1.556509 13.851305
S0.1.16 6.0 6.737475 -0.338474 15.069340
S0.1.18 2.0 3.404263 -4.309009 11.098805
S0.1.19 2.0 2.790780 -4.984485 10.423329
S0.1.20 4.0 3.404263 -4.309009 11.098805
S1.1.01 24.0 25.908098 16.967949 32.375763
S1.1.02 26.0 25.908098 19.213318 34.621132
S1.1.03 16.0 16.706976 9.052378 24.460192
S1.1.04 48.0 48.135574 40.286213 55.694027
S1.1.06 16.0 15.921209 8.255709 23.663523
S1.1.07 43.0 45.408558 37.756302 53.164116
S1.1.08 17.0 17.444435 9.634922 25.042736
sample prediction lower upper
S2.1.01 34.574070 26.870163 42.277977
S2.1.02 34.793640 27.089733 42.497547
S2.1.03 30.931238 23.227331 38.635147
S2.1.04 42.754295 35.050388 50.458202
S2.1.05 36.664867 28.960960 44.368774
S2.1.06 34.712154 27.008247 42.416061
S2.1.07 45.460209 37.756302 53.164116
S2.1.08 32.273796 24.569889 39.977703
S2.1.09 51.157234 43.453327 58.861141
S2.1.10 46.247551 38.543644 53.951458
S2.1.11 34.712154 27.008247 42.416061
S2.1.12 53.703907 46.000000 61.407814
S2.1.13 30.889977 23.186069 38.593884
S2.1.14 29.686930 21.983023 37.390839
S2.1.15 3.394899 -4.309009 11.098805
S2.1.16 11.982977 4.279070 19.686884
S2.1.17 30.852961 23.149054 38.556870
S2.1.18 6.469562 -1.234345 14.173469
S2.1.19 2.719422 -4.984485 10.423329
S2.1.20 3.394899 -4.309009 11.098805
Ricerca Corrente - IZS AM 06/24 RC: "genomic data-based machine learning to predict categorical and continuous phenotypes by classification and regression".
Many thanks to Andrea De Ruvo, Adriano Di Pasquale and ChatGPT for the insightful discussions that helped improve the algorithm.
An article might potentially be published in the future.
Please cite: GitHub (https://github.com/Nicolas-Radomski/GenomicBasedRegression), Docker Hub (https://hub.docker.com/r/nicolasradomski/genomicbasedregression), and/or Anaconda Hub (https://anaconda.org/nicolasradomski/genomicbasedregression).
Nicolas Radomski