- By:
- Gao, Mu; Lund-Andersen, Peik; Morehead, Alex; Mahmud, Sajid; Chen, Chen; Chen, Xiao; Giri, Nabin; Roy, Raj; Quadir, Farhan; Effler, Timothy C; Prout, Ryan C; Abraham, Subil ; Skolnick, Jeffrey; Cheng, Jianlin; Sedova, Ada A; Elwasif, Wael R; Haas, Nicholas Q
- Page Number:
- 46-57
- Book Title:
- 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)
- Publication Date:
- November 9, 2023
- Publisher Location:
- IEEE Xplore, United States of America
- Conference Name:
- 7th workshop on Machine Learning in High Performance Computing Environments (MLHPC)
- Conference Location:
- St. Loius, Missouri, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/MLHPC54614.2021.00010
Abstract
Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.
