Using interpretable deep learning to model cancer dependencies | Zendy

Chih-Hsu Lin | Zendy; Olivier Lichtarge | Zendy

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Using interpretable deep learning to model cancer dependencies

Author(s) -

Chih-Hsu Lin,

Olivier Lichtarge

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab137

Subject(s) - interpretability , dependency (uml) , computer science , artificial neural network , artificial intelligence , machine learning , deep learning , feature (linguistics) , field (mathematics) , source code , code (set theory) , data mining , mathematics , philosophy , linguistics , set (abstract data type) , pure mathematics , programming language , operating system

Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field.

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