Multi-label pathway prediction based on active dataset subsampling

M. A. Basher, A. R.; Hallam, S.

doi:10.1101/2020.09.14.297424

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bioRxiv · 10.1101/2020.09.14.297424

Multi-label pathway prediction based on active dataset subsampling

M. A. Basher, A. R.·Hallam, S.

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Abstract

Metabolic pathways are composed of reaction sequences catalyzed by enzymes. The set of reactions within and between cells comprises a reactome. Pathways and reactomes can be predicted from organismal or multi-organismal genomes using rule-based or machine learning methods. While machine learning methods overcome issues of probability and scale associated with rule-based methods, several complications remain that can degrade performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of pathways within a dataset. Here, we present leADS (multi-label learning based on active dataset subsampling), a machine learning method, that uses subsampling to reduce the negative impact of training loss due to class imbalance. We demonstrate leADs performance using organismal and multi-organismal datasets in relation to other machine learning pathway prediction methods. Availability and implementationleADS is available under the GNU license at github.com/hallamlab/leADS. A wiki, including a tutorial, is available at github.com//hallamlab/leADS/wiki Contactshallam@mail.ubc.ca

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BibTeX RIS

M. A. Basher, A. R., Hallam, S.. 2020-09-16. Multi-label pathway prediction based on active dataset subsampling. https://doi.org/10.1101/2020.09.14.297424

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