Work

Publisher Correction: Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma

Year: 2020

Type: erratum

Source: Nature Reviews Gastroenterology & Hepatology

DOI: 10.1038/s41575-020-0288-6

Authors: Bin Chen, Lana Garmire, Diego F. Calvisi, Mei-Sze Chua, Robin K. Kelley, Xin Chen

Institutions: Michigan State University, University of Michigan, University of Sassari, University of Regensburg, Stanford Medicine, Stanford University, University of California, San Francisco

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Institute of General Medical Sciences, National Institute of Environmental Health Sciences, U.S. National Library of Medicine, National Center for Advancing Translational Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: Targeting glucose metabolism for the treatment of Hepatocellular Carcinoma, DR. EPS: Drug Repurposing for Extended Patient Survival, An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer Risks, Institute for Biogenesis Research: COBRE, An Integrative Bioinformatics Approach to Study Single Cancer Cell Heterogeneity, Integrating transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers, Cancer precision medicine through spatially informative single cell image and transcriptomics data analysis

OpenAlex ID: W3010933801

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