exposomeProjUvA

This GitHub repository shares the scripts in Julia and Python programming languages that are used for models' development for the work entitled "Machine Learning for Enhanced Identification Probability in RPLC/HRMS Non-Targeted Workflows".

The 3 machine learning models that are developed in this project "exposomeProjUvA" are available at

https://bitbucket.org/hiulokngan/modelsExposomeProjUvA/

Published: 12 August 2025, DOI: 10.1021/acs.analchem.5c01873

Hiu-Lok Ngan,† Viktoriia Turkina,‡ Denice van Herwerden,‡ Hong Yan,†,‖ Zongwei Cai,,† and Saer Samanipour,‡,§

† State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, P. R. China

‖ Department of Biology, Hong Kong Baptist University, Hong Kong, P. R. China

‡ Van ’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands

§ UvA Data Science Center, University of Amsterdam, Amsterdam, 1012 WP, the Netherlands

* Corresponding authors: Drs. Zongwei Cai and Saer Samanipour

E-mails: [email protected]; [email protected]

Procedure

0_tools (folder)

A_Installer11.ipynb

1_model1 (folder)

A_CombinePubchemFP.jl

- INPUT(S): dataPubchemFingerprinter.csv

- OUTPUT(S): dataPubchemFingerprinter_converted.csv

B_MergeApc2dPubchem.jl

- INPUT(S): dataAP2DFingerprinter.csv

- INPUT(S): dataPubchemFingerprinter_converted.csv

- OUTPUT(S): dataAllFingerprinter_4RiPredict.csv

C_ModelDeploy4FPbasedRi.jl

- INPUT(S): dataAllFingerprinter_4RiPredict.csv

- INPUT(S): CocamideExtendedWithStratification.joblib

- OUTPUT(S): dataAllFP_withNewPredictedRiWithStratification.csv

D_FeatureCorrelation.ipynb

2_model2 (folder)

A_PreProcessInternalDB.jl (README_dbColHeaders.md)

- INPUT(S): Database_INTERNAL_2022-11-17.csv

- INPUT(S): CNLs_10mDa.csv

- INPUT(S): dataAllFP_withNewPredictedRiWithStratification.csv

- OUTPUT(S): CNLs_10mDa_missed.csv

- OUTPUT(S): databaseOfInternal_withNLs.csv

- OUTPUT(S): dataframeCNLsRows.csv

- OUTPUT(S): dfCNLsSumModeling.csv

- OUTPUT(S): massesCNLsDistrution.png

B_FPDfPre4Leverage.jl

- INPUT(S): databaseOfInternal_withNLs.csv

- INPUT(S): dataframeCNLsRows.csv

- INPUT(S): dataAllFP_withNewPredictedRiWithStratification.csv

- OUTPUT(S): countingRows4Leverage.csv

- OUTPUT(S): countingRowsInFP4Leverage.csv

- OUTPUT(S): dataAllFP_withNewPredictedRiWithStratification_Freq.csv

C_TrainTestSplitPre.jl

- INPUT(S): dataframeCNLsRows.csv

- INPUT(S): dataAllFP_withNewPredictedRiWithStratification_Freq.csv

- OUTPUT(S): databaseOfInternal_withNLsOnly.csv

- OUTPUT(S): databaseOfInternal_withEntryInfoOnly.csv

- OUTPUT(S): databaseOfInternal_withINCHIKEYInfoOnly.csv

- OUTPUT(S): databaseOfInternal_withYOnly.csv

D_LeverageGetIdx.jl

- INPUT(S): dataAllFP_withNewPredictedRiWithStratification_Freq.csv

- OUTPUT(S): dataframe73_dfTrainSetWithStratification_95index.csv

- OUTPUT(S): dataframe73_dfTestSetWithStratification_95index.csv

- OUTPUT(S): dataframe73_dfWithStratification_95index.csv

- OUTPUT(S): dataAllFP73_withNewPredictedRiWithStratification_FreqAnd95Leverage.csv

E_TrainTestSplit.jl

- INPUT(S): databaseOfInternal_withEntryInfoOnly.csv

- INPUT(S): databaseOfInternal_withINCHIKEYInfoOnly.csv

- INPUT(S): databaseOfInternal_withNLsOnly.csv

- INPUT(S): databaseOfInternal_withYOnly.csv

- INPUT(S): dataframe73_dfTrainSetWithStratification_95index.csv

- INPUT(S): dataframe73_dfTestSetWithStratification_95index.csv

- OUTPUT(S): dataframe73_95dfTrainSetWithStratification.csv

- OUTPUT(S): dataframe73_95dfTestSetWithStratification.csv

F_CNLmodelTrainValTest.jl

- INPUT(S): dataframe73_95dfTestSetWithStratification.csv

- INPUT(S): dataframe73_95dfTrainSetWithStratification.csv

- INPUT(S): CocamideExtWithStartification_Fingerprints_train.csv

- INPUT(S): CocamideExtWithStratification_Fingerprints_test.csv

- INPUT(S): dataAllFP_withNewPredictedRiWithStratification.csv

- OUTPUT(S): hyperparameterTuning_RFwithStratification10F.csv

- OUTPUT(S): CocamideExtended73_CNLsRi_RFwithStratification.joblib

- OUTPUT(S): dataframe73_dfTrainSetWithStratification_withCNLPredictedRi.csv

- OUTPUT(S): dataframe73_dfTestSetWithStratification_withCNLPredictedRi.csv

- OUTPUT(S): dataframe73_dfTrainSetWithStratification_withCNLPredictedRi_withCocamides.csv

- OUTPUT(S): dataframe73_dfTestSetWithStratification_withCNLPredictedRi_withCocamides.csv

- OUTPUT(S): CNLRiPrediction73_RFTrainWithStratification_v3.png

- OUTPUT(S): CNLRiPrediction73_RFTestWithStratification_v3.png

G_CNLdfLeverage.jl

- INPUT(S): dataframe73_95dfTrainSetWithStratification.csv

- INPUT(S): dataframe73_dfTrainSetWithStratification_95FPCNLleverage.csv

- OUTPUT(S): CNL model 95% leverage cut-off = 0.14604417882015916

- OUTPUT(S): CNLLeverageValueDistrution.png

H_Results.jl

I_EDA_Results.jl

J_FeatureCorrelation.ipynb

3_model3 (folder)

3_I_prepareSemisynData (sub-folder)

A_DataSplitMatch.jl

- INPUT(S): Cand_synth_rr10_1_1000.csv

- INPUT(S): Cand_synth_rr10_1001_2000.csv

- INPUT(S): Cand_synth_rr10_2001_3000.csv

- INPUT(S): Cand_synth_rr10_3001_4000.csv

- INPUT(S): Cand_synth_rr10_4001_5000.csv

- INPUT(S): dataAllFP_withNewPredictedRiWithStratification.csv

- INPUT(S): generated_susp.csv

- OUTPUT(S): Cand_synth_rr10_1-5000.csv

- OUTPUT(S): Cand_synth_rr10_1-5000_extractedWithoutDeltaRi.csv

- OUTPUT(S): Cand_synth_rr10_1-5000_extractedWithoutDeltaRi_trainValDf.csv

- OUTPUT(S): Cand_synth_rr10_1-5000_extractedWithoutDeltaRi_isotestDf.csv

B_DfCNLdeploy.jl

- INPUT(S): Cand_synth_rr10_1-5000_extractedWithoutDeltaRi_trainValDf.csv or Cand_synth_rr10_1-5000_extractedWithoutDeltaRi_isotestDf.csv

- INPUT(S): dataframe73_dfTestSetWithStratification_withCNLPredictedRi.csv

- INPUT(S): CocamideExtended73_CNLsRi_RFwithStratification.joblib

- OUTPUT(S): TPTN_dfCNLfeaturesStr.csv

- OUTPUT(S): Cand_synth_rr10_1-5000_extractedWithCNLsList.csv or Cand_synth_rr10_1-5000_extractedWithCNLsList_pest.csv

- OUTPUT(S): dfCNLsSum_1.csv - dfCNLsSum_8.csv or dfCNLsSum_pest.csv

- OUTPUT(S): TPTNmassesCNLsDistrution_8.png

- OUTPUT(S): dataframeCNLsRows4TPTNModeling_8withCNLRideltaRi.csv or dataframeCNLsRows4TPTNModeling_PestwithCNLRideltaRi.csv

- OUTPUT(S): dataframeCNLsRows4TPTNModeling_TPOnlywithCNLRideltaRi.csv or dataframeCNLsRows4TPTNModeling_TPOnlywithCNLRideltaRi_pest.csv

- OUTPUT(S): dfCNLsSum_TP.csv or dfCNLsSum_TP_pest.csv

- OUTPUT(S): dfCNLsSum.csv

- OUTPUT(S): TPTNmassesCNLsDistrution.png or TPTNmassesCNLsDistrution_pest.png

C_TPTNmodelCNLleverageCutoff.jl

- INPUT(S): dataframeCNLsRows4TPTNModeling_1withCNLRideltaRi.csv - dataframeCNLsRows4TPTNModeling_8withCNLRideltaRi.csv or dataframeCNLsRows4TPTNModeling_PestwithCNLRideltaRi.csv

- OUTPUT(S): dataframeTPTNModeling_1.csv - dataframeTPTNModeling_8.csv or dataframeTPTNModeling_pest.csv

D_TPTNmodelTrainTestSplit_ALL.jl

- INPUT(S): dataframeTPTNModeling_1.csv - dataframeTPTNModeling_8.csv and dataframeTPTNModeling_pest.csv

- OUTPUT(S): dataframeTPTNModeling_all.csv

- OUTPUT(S): dataframeTPTNModeling_withLeverage_all.csv

- OUTPUT(S): dataframeTPTNModeling_TrainIndex_all.csv

- OUTPUT(S): dataframeTPTNModeling_TrainDFwithhl_all.csv

- OUTPUT(S): dataframeTPTNModeling_TrainYesIndex_all.csv

- OUTPUT(S): dataframeTPTNModeling_TrainYesDFwithhl_all.csv

- OUTPUT(S): dataframeTPTNModeling_TestIndex_all.csv

- OUTPUT(S): dataframeTPTNModeling_TestDFwithhl_all.csv

- OUTPUT(S): dataframeTPTNModeling_TestYesIndex_all.csv

- OUTPUT(S): dataframeTPTNModeling_TestYesDFwithhl_all.csv

3_II_readyPestData (sub-folder)

E_TestSamplesPre.jl

- INPUT(S): CNL_Ref.csv

- OUTPUT(S): CNL_Ref_PestMix_1-8.csv

- OUTPUT(S): INCHIKEYs_CNL_Ref_PestMix_1-8.csv

- OUTPUT(S): INCHIKEYs_CNL_Ref_PestMix_1.csv - INCHIKEYs_CNL_Ref_PestMix_8.csv

F_TestTemplate.jl (for test samples in the foler- PestMix1-8_test_report_comp_IDs)

- INPUT(S): INCHIKEYs_CNL_Ref_PestMix_1-8.csv

- INPUT(S): PestMix1-8_test_report_comp_IDs.csv

- INPUT(S): dataAllFP_withNewPredictedRiWithStratification.csv

- INPUT(S): TPTN_dfCNLfeaturesStr.csv

- INPUT(S): CocamideExtended73_CNLsRi_RFwithStratification.joblib

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_check.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_checked.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_ready4CNLdf.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_extractedWithCNLsList.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_withCNLRideltaRi.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_dataframeTPTNModeling.csv

3_III_EDA (sub-folder)

G_PreEDA.jl

- INPUT(S): PestMix1-8_1ug-L_NoTea_test_report_comp_IDs_dataframeTPTNModeling.csv

- INPUT(S): PestMix1-8_1ug-L_Tea_test_report_comp_IDs_dataframeTPTNModeling

- INPUT(S): dataframeTPTNModeling_TrainYesDFwithhl_all.csv

- INPUT(S): dataframeTPTNModeling_TestYesDFwithhl_all.csv

- OUTPUT(S): allRealsampleNoTea_dataframeTPTNModeling.csv

- OUTPUT(S): allRealsampleWithTea_dataframeTPTNModeling.csv

- OUTPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- OUTPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- OUTPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- OUTPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- OUTPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- OUTPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- OUTPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- OUTPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- OUTPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- OUTPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- OUTPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- OUTPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

H_EDA_RefMatchFragRatio.jl

I_EDA_UsrMatchFragRatio.jl

J_EDA_MS1Error.jl

K_EDA_MS2Error.jl

L_EDA_MS2ErrorStd.jl

M_EDA_Match.jl

N_EDA_FinalScoreRatio.jl

O_EDA_MonoisotopicMass.jl

P_EDA_DeltaRI.jl

- INPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- INPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- INPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- INPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- INPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- INPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- INPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatio.csv

- INPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDE.csv

- INPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- OUTPUT(S): outplot_TPTNDistrution_FeatureRefMatchFragRatio_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureUsrMatchFragRatio_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureMS1Error_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureMS2Error_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureMS2ErrorStd_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureMatch_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureFinalScoreRatio_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureMonoisotopicMass_noFilter.png

- OUTPUT(S): outplot_TPTNDistrution_FeatureDeltaRi_noFilter.png

3_IV_featureModelSelection (sub-folder)

Q_FeatureSelection.ipynb

- INPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- OUTPUT(S): sns_heatmap( )

R_FeatureModelSelection.jl

- INPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- OUTPUT(S): hyperparameterTuning_modelSelection_RF(n)_noFilter_noLog(UsrFragMatchRatio).csv

- OUTPUT(S): hyperparameterTuning_modelSelection_DT(n)_noFilter_noLog(UsrFragMatchRatio).csv

- OUTPUT(S): hyperparameterTuning_modelSelection_KNN(n)_noLog(UsrFragMatchRatio)_noMatchDiff.csv

- OUTPUT(S): hyperparameterTuning_modelSelection_KNN(n)_noLog(UsrFragMatchRatio)_noMatchDiff_noDeltaRI.csv

- OUTPUT(S): hyperparameterTuning_modelSelection_LR(n)_noFilter_noLog(UsrFragMatchRatio).csv"

T_Plot4ModelFeatureSelection.ipynb

- INPUT(S): hyperparameterTuning_modelSelection_LR_noFilterSummary.xlsx

- INPUT(S): hyperparameterTuning_modelSelection_DT_noFilterSummary.xlsx

- INPUT(S): hyperparameterTuning_modelSelection_RF_noFilterSummary.xlsx

- INPUT(S): hyperparameterTuning_modelSelection_KNN_noFilterSummary.xlsx

- OUTPUT(S): updated4pestNoTea_8paraVS7para.jpg

- OUTPUT(S): updated4pest_8paraVS7para.jpg

- OUTPUT(S): updatedPestRecall_8paraVS7para.jpg

U_FeatureImportance.ipynb

3_V_modelEvaluation (sub-folder)

U_ModelEvaluation.jl

- INPUT(S): trainDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): testDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): noTeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- INPUT(S): TeaDF_dataframeTPTNModeling_0d5FinalScoreRatioDEnoFilterSTD.csv

- OUTPUT(S): modelTPTNModeling_6paraKNN_noFilterWithDeltaRI.joblib

- OUTPUT(S): modelTPTNModeling_6paraKNN_noFilterWithOutDeltaRI.joblib

- OUTPUT(S): dataframeTPTNModeling_TrainDF_withDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_TrainDF_withOutDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_TrainDF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_TrainDF_withOutDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_ValDF_withDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_ValDF_withOutDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_ValDF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_ValDF_withOutDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_PestDF_withDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_PestDF_withOutDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_PestDF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_PestDF_withOutDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_Pest2DF_withDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_Pest2DF_withOutDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_Pest2DF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframeTPTNModeling_Pest2DF_withOutDeltaRIandPredictedTPTNandpTP_KNN.csv

V_TPTNmodelCutOff.jl

- INPUT(S): dataframeTPTNModeling_TrainDF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- INPUT(S): dataframeTPTNModeling_ValDF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- INPUT(S): dataframeTPTNModeling_PestDF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- INPUT(S): dataframeTPTNModeling_Pest2DF_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): dataframePostPredict_TrainALLWithDeltaRI_KNN.csv

- OUTPUT(S): dataframePostPredict_TestALLWithDeltaRI_KNN.csv

- OUTPUT(S): dataframePostPredict_PestNoTeaWithDeltaRI_KNN.csv

- OUTPUT(S): dataframePostPredict_Pest2WithTeaWithDeltaRI_KNN.csv

- OUTPUT(S): TPTNPrediction_KNNtrainTestCM.png

- OUTPUT(S): TPTNPrediction_KNNpestPest2CM.png

- OUTPUT(S): dataframePostPredict_TPRFNRFDR_newTrainALL_KNN.csv

- OUTPUT(S): dataframePostPredict_TPRFNRFDR_newTestALL_KNN.csv

- OUTPUT(S): dataframePostPredict_TPRFNRFDR_newPestNoTea_KNN.csv

- OUTPUT(S): TPTNPrediction_P1threshold2TPRFNRFDR_newTrainALLylims_KNN.png

- OUTPUT(S): dataframePostPredict10FDR_TrainALLWithDeltaRI_KNN.csv

- OUTPUT(S): dataframePostPredict10FDR_TestALLWithDeltaRI_KNN.csv

- OUTPUT(S): dataframePostPredict10FDR_PestNoTeaWithDeltaRI_KNN.csv

- OUTPUT(S): dataframePostPredict10FDR_Pest2WithTeaWithDeltaRI_KNN.csv

W_FDRcontrolledResults.ipynb

4_application (folder)

A_TestTemplate_ALL.jl

- INPUT(S): PestMix1-8_test_report_comp_IDs_dataframeTPTNModeling.csv

- INPUT(S): modelTPTNModeling_6paraKNN_noFilterWithDeltaRI.joblib

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_dataframeTPTNModeling_0d5FinalScoreRatioDEFilterSTD.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_withDeltaRIandPredictedTPTN_KNN.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_withDeltaRIandPredictedTPTNandpTP_KNN.csv

- OUTPUT(S): PestMix1-8_test_report_comp_IDs_withDeltaRIandPredictedTPTNandpTP_KNN_ind.csv