1. Accelerated knowledge discovery from omics data by optimal experimental design.
- Author
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Wang, Xiaokang, Rai, Navneet, Merchel Piovesan Pereira, Beatriz, Eetemadi, Ameen, and Tagkopoulos, Ilias
- Subjects
Escherichia coli ,Bacterial Proteins ,Membrane Proteins ,Molecular Chaperones ,Anti-Bacterial Agents ,Disinfectants ,Computational Biology ,Gene Expression Regulation ,Bacterial ,Models ,Biological ,Research Design ,Stress ,Physiological ,Machine Learning ,Gene Expression Regulation ,Bacterial ,Models ,Biological ,Stress ,Physiological - Abstract
How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.
- Published
- 2020