ArticleBMC bioinformatics2020
Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning.Patterns (New York, N.Y.) · 2025Article
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.Artificial intelligence in medicine · 2025Article
- Leveraging AutoML to optimize dataset selection for improved breast cancer variants pathogenicity prediction.Computational and structural biotechnology journal · 2025Article
- Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.BMC medical genomics · 2024Article
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.bioRxiv : the preprint server for biology · 2024Article
- Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.Research square · 2024Article
- MiTree: A Unified Web Cloud Analytic Platform for User-Friendly and Interpretable Microbiome Data Mining Using Tree-Based Methods.Microorganisms · 2023Article
- A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.Journal of medical systems · 2023Article
- Automated quantitative trait locus analysis (AutoQTL).BioData mining · 2023Article
- Reducing the complexity of high-dimensional environmental data: An analytical framework using LASSO with considerations of confounding for statistical inference.International journal of hygiene and environmental health · 2023Article
- The promise of automated machine learning for the genetic analysis of complex traits.Human genetics · 2022Review
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- Leveraging Automated Machine Learning for the Analysis of Global Public Health Data: A Case Study in Malaria.International journal of public healthArticle
- Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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5 authors.
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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.
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