Work

Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models

Year: 2023

Type: article

Source: Nature Biomedical Engineering

DOI: 10.1038/s41551-023-01034-0

Authors: Joseph D. Janizek, Ayse B. Dincer, Safiye Celik, Hugh Chen, William Chen, Kamila Naxerova, Su-In Lee

Institutions: University of Washington, Recursion (United States), Harvard University, Massachusetts General Hospital, Center for Systems Biology

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute on Aging, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of General Medical Sciences

Awards: Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets, Illuminating the evolutionary history of colorectal cancer metastasis: basic principles and clinical applications, Opening the Black Box of Machine Learning Models, Hematopoiesis in Cardiovascular Disease

OpenAlex ID: W4367609826

FWCI: 5.693

Cites: 121

Cited by: 66

Related to: 10