TriGORank: A Gene Ontology Enriched Learning-to-Rank Framework for Trigenic Fitness Prediction

Sahiti Labhishetty, Ismini Lourentzou, Michael Jeffrey Volk, Shekhar Mishra, Huimin Zhao, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning (ML) has been gaining interest in the metabolic engineering community as a means to automate prediction tasks. In this work, we introduce and study the task of using ML to recommend high-fitness triplet mutants as candidates for wet-lab experiments. We first utilize individual fitness and digenic fitness scores as features and train machine learning models that produce a ranked list, from high to low fitness s cores, f or triplet gene mutants of S. cerevisiae. Then, we incorporate prior metabolic knowledge from an existing gene ontology, by designing a novel graph representation and deducing features that can capture gene similarity and gene interactions. Experimental results show that our proposed gene ontology enriched model, termed TriGORank, improves both performance and explainability.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1841-1848
Number of pages8
ISBN (Electronic)9781665401265
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States
Duration: Dec 9 2021Dec 12 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/9/2112/12/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics
  • Information Systems and Management

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