about
Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds.Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD.Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction.Prediction of Compound Profiling Matrices Using Machine Learning.Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training DataMulti-unit calibration rejects inherent device variability of chemical sensor arraysOveroptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic studyInterpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley ValuesMachine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding ModesInterpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
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description
onderzoeker
@nl
researcher, ORCID id # 0000-0002-2992-3402
@en
name
Raquel Rodríguez-Pérez
@ast
Raquel Rodríguez-Pérez
@en
Raquel Rodríguez-Pérez
@es
Raquel Rodríguez-Pérez
@nl
type
label
Raquel Rodríguez-Pérez
@ast
Raquel Rodríguez-Pérez
@en
Raquel Rodríguez-Pérez
@es
Raquel Rodríguez-Pérez
@nl
prefLabel
Raquel Rodríguez-Pérez
@ast
Raquel Rodríguez-Pérez
@en
Raquel Rodríguez-Pérez
@es
Raquel Rodríguez-Pérez
@nl
P106
P1153
57194069554
57201195808
P31
P496
0000-0002-2992-3402