1. Leveraging heterogeneous network embedding for metabolic pathway prediction
- Author
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Basher, Abdur Rahman M. A. and Hallam, Steven J.
- Subjects
Statistics and Probability ,AcademicSubjects/SCI01060 ,Computer science ,Population ,02 engineering and technology ,Machine learning ,computer.software_genre ,Biochemistry ,Machine Learning ,03 medical and health sciences ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Leverage (statistics) ,education ,Molecular Biology ,030304 developmental biology ,chemistry.chemical_classification ,0303 health sciences ,education.field_of_study ,business.industry ,Systems Biology ,MetaCyc ,Proteins ,Genomics ,Original Papers ,Computer Science Applications ,Visualization ,Computational Mathematics ,Metabolic pathway ,Enzyme ,ComputingMethodologies_PATTERNRECOGNITION ,Computational Theory and Mathematics ,chemistry ,Embedding ,Artificial intelligence ,business ,Heuristics ,computer ,Heterogeneous network ,Metabolic Networks and Pathways ,Software - Abstract
Motivation Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. Results Here, we present pathway2vec, a software package consisting of six representational learning modules used to automatically generate features for pathway inference. Specifically, we build a three-layered network composed of compounds, enzymes and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve prediction outcomes. Availability and implementation The software package and installation instructions are published on http://github.com/pathway2vec. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2020