Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease.

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Bibliographic Details
Title: Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease.
Authors: Burkhart JG; Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA., Wu G; Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA., Song X; Department of Computer Science and Electrical Engineering, Oregon Health & Science University, Portland, OR 97239, USA., Raimondi F; BIO@SNS, Scuola Normale Superiore di Pisa, 56126 Pisa, Italy., McWeeney S; Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA., Wong MH; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97201, USA., Deng Y; Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA.
Source: Patterns (New York, N.Y.) [Patterns (N Y)] 2023 May 22; Vol. 4 (7), pp. 100758. Date of Electronic Publication: 2023 May 22 (Print Publication: 2023).
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101767765 Publication Model: eCollection Cited Medium: Internet ISSN: 2666-3899 (Electronic) Linking ISSN: 26663899 NLM ISO Abbreviation: Patterns (N Y) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2666-3899
DOI:10.1016/j.patter.2023.100758