Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Appli...
Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection
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Publisher
Switzerland: MDPI AG
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Language
English
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Publisher
Switzerland: MDPI AG
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Contents
The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we prop...
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Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection
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TN_cdi_proquest_miscellaneous_3104539903
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_3104539903
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ISSN
1422-0067,1661-6596
E-ISSN
1422-0067
DOI
10.3390/ijms25179576
