TY - GEN
T1 - TriGORank
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
AU - Labhishetty, Sahiti
AU - Lourentzou, Ismini
AU - Volk, Michael Jeffrey
AU - Mishra, Shekhar
AU - Zhao, Huimin
AU - Zhai, Chengxiang
N1 - Funding Information:
ACKNOWLEDGMENT This work was funded by U.S. Department of Energy award DE-SC0018420. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy. REFERENCES
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85125188600&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125188600&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669503
DO - 10.1109/BIBM52615.2021.9669503
M3 - Conference contribution
AN - SCOPUS:85125188600
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 1841
EP - 1848
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2021 through 12 December 2021
ER -