Multimodal Machine Learning for Drug Knowledge Discovery

Guy Shtar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Multimodal machine learning deals with building models that can process information from multiple modalities (i.e., ways of doing or experiencing something). Experiments involving humans are used to guarantee drug safety in the complex task of drug development. Drug-related data is readily available and comes in various modalities. The proposed study aims to develop novel methods for multimodal machine learning that can be used to process the diverse multimodal data used in drug development and other challenging tasks that could benefit from the use of multimodal data. We present a series of drug-related tasks which are used to both evaluate the models proposed in this ongoing study and discover new drug knowledge. This research will make far-reaching contributions to the field of machine learning, as well as practical contributions in the medical domain.

Original languageEnglish
Title of host publicationWSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages1115-1116
Number of pages2
ISBN (Electronic)9781450382977
DOIs
StatePublished - 3 Aug 2021
Event14th ACM International Conference on Web Search and Data Mining, WSDM 2021 - Virtual, Online, Israel
Duration: 8 Mar 202112 Mar 2021

Publication series

NameWSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Conference

Conference14th ACM International Conference on Web Search and Data Mining, WSDM 2021
Country/TerritoryIsrael
CityVirtual, Online
Period8/03/2112/03/21

Keywords

  • drug knowledge
  • multimodal machine learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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