Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Background A 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 t...

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Veröffentlicht in:	BMC bioinformatics Jg. 21; H. 1; S. 430 - 13
Hauptverfasser:	Manduchi, Elisabetta, Fu, Weixuan, Romano, Joseph D., Ruberto, Stefano, Moore, Jason H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 01.10.2020 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Automation AutoML Big Data Bioinformatics Biomedical and Life Sciences Biomedical data Cell adhesion & migration Computational Biology/Bioinformatics Computer Appl. in Life Sciences Covariate adjustment Creatinine Data Analysis Datasets Drug dosages Embedding Feature importance Gene expression Genetic algorithms Genetic programming Genomics Humans Information management Kidney diseases Kinases Learning algorithms Life Sciences Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Mental disorders Methodology Methodology Article Microarrays Optimization Pathways Prediction models Proteins Regression analysis Schizophrenia Technology application Variables Pathways AutoML Genetic programming Feature importance Covariate adjustment
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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