ArticleWorkshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments2021
High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.
Article in Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM.Nature communications · 2024Article
- Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.Scientific data · 2024Article
- De Novo Atomic Protein Structure Modeling for Cryo-EM Density Maps Using 3D Transformer and Hidden Markov Model.bioRxiv : the preprint server for biology · 2024Article
- Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.bioRxiv : the preprint server for biology · 2024Article
- A gated graph transformer for protein complex structure quality assessment and its performance in CASP15.Bioinformatics (Oxford, England) · 2023Article
- Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.BMC bioinformatics · 2022Article
- AF2Complex predicts direct physical interactions in multimeric proteins with deep learning.Nature communications · 2022Article
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Authors and funding
17 authors.
Funding
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.
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