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Machine Learning for Chemogenomics on HPC in the ExCAPE Project

Authors:
Tom Vander Aa
Tom Ashby
Yves Vandriessche
Vojtech Cima
Stanislav Böhm
Jan Martinovic

Keywords: Machine Learning, High-Performance Computing, Collaborative Filtering, Distributed Task Scheduling

Abstract:
The ExCAPE project is a Horizon 2020 project to advance the state of the art of machine learning (ML) implementations on supercomputing hardware. We have adopted bioactivity predictions for chemogenomics as a challenging use-case to drive development. In this paper, we will give an overview of the challenges in ExCAPE to use supercomputing efficiently. We will touch on three key examples dealing with efficient ML workflow execution, support for multi-task learning using matrix factorization methods and the challenges originating from the large and very sparse datasets in ExCAPE.

Pages: 72 to 74

Copyright: Copyright (c) IARIA, 2017

Publication date: June 25, 2017

Published in: conference

ISSN: 2308-3484

ISBN: 978-1-61208-567-8

Location: Venice, Italy

Dates: from June 25, 2017 to June 29, 2017