Abstract
BackgroundA typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis.ResultsWe developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj.ConclusionsIn this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.
Highlights
A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model
A desirable feature of Tree-based Pipeline Optimization Tool (TPOT) is the ability to adjust for relevant covariates, as this is important in the biomedical context where there are often either baseline characteristics of the subjects or batch effects whose influence on the target or the features needs to be removed so to isolate the actual effects of the features on the target
TG‐GATEs To fully exploit this large expression data set to identify pathways and genes directly associated to creatinine levels, we needed to factor out the confounding effect of compound treatment
Summary
A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. In biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. The Tree-based Pipeline Optimization Tool (TPOT) [1, 2] is a genetic programming (GP) based AutoML which has been successfully used in biomedical applications. A desirable feature of TPOT is the ability to adjust for relevant covariates, as this is important in the biomedical context where there are often either baseline characteristics of the subjects or batch effects whose influence on the target or the features needs to be removed so to isolate the actual effects of the features on the target. It is important to note that, while common in biostatistics and epidemiology, covariate adjustment is uncommon and understudied in machine learning
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