Interpretable machine learning methods for predictions in systems biology from omics data
- Author(s)
- David Sidak, Jana Schwarzerová, Wolfram Weckwerth, Steffen Waldherr
- Abstract
Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability?” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.
- Organisation(s)
- Functional and Evolutionary Ecology, Research Platform Vienna Metabolomics Center
- External organisation(s)
- Brno University of Technology, Universität Wien
- Journal
- Frontiers in Molecular Biosciences
- Volume
- 9
- ISSN
- 2296-889X
- DOI
- https://doi.org/10.3389/fmolb.2022.926623
- Publication date
- 10-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 106005 Bioinformatics, 106057 Metabolomics
- Keywords
- ASJC Scopus subject areas
- Biochemistry, Molecular Biology, Biochemistry, Genetics and Molecular Biology (miscellaneous)
- Sustainable Development Goals
- SDG 3 - Good Health and Well-being
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/interpretable-machine-learning-methods-for-predictions-in-systems-biology-from-omics-data(7726e2c0-a027-4451-b9be-2a327a1062bd).html