Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments最新文献

英文中文

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function. 用于蛋白质结构和功能基因组级预测的高性能深度学习工具箱。

Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments

Pub Date : 2021-11-01 DOI: 10.1109/mlhpc54614.2021.00010

Mu Gao, Peik Lund-Andersen, Alex Morehead, Sajid Mahmud, Chen Chen, Xiao Chen, Nabin Giri, Raj S Roy, Farhan Quadir, T Chad Effler, Ryan Prout, Subil Abraham, Wael Elwasif, N Quentin Haas, Jeffrey Skolnick, Jianlin Cheng, Ada Sedova

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.

随着高性能计算(HPC)的出现，计算生物学是许多需要创新和加速的科学学科之一。近年来，机器学习领域也从采用高性能计算实践中获益良多。在这项工作中，我们提出了一种新的HPC管道，该管道结合了各种机器学习方法，用于在全基因组规模上对蛋白质进行基于结构的功能注释。我们的产品线广泛使用深度学习，并为高通量数据(如蛋白质组学数据)训练高级深度学习模型的最佳实践提供计算见解。我们展示了我们的管道目前支持的方法，并详细介绍了我们的管道要包膜的未来任务，包括使用SAdLSA进行大规模序列比较和使用AlphaFold2预测蛋白质三级结构。

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引用次数: 10

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments

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