Uncovering Heterogeneous Effects via Localized Feature Selection.

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Bibliographic Details
Title: Uncovering Heterogeneous Effects via Localized Feature Selection.
Authors: Liu X; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA., Gu J; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA., Chen Z; Department of Statistics, Stanford University, Stanford, CA 94305, USA., Chu B; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA., Liu L; Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA., Morrison T; Department of Statistics, Stanford University, Stanford, CA 94305, USA., Butler RR 3rd; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA., Edelson J; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA., Li J; Department of Statistics, Stanford University, Stanford, CA 94305, USA., Longo FM; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA., Tang H; Department of Genetics, Stanford University, Stanford, CA 94305, USA., Ionita-Laza I; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA., Sabatti C; Department of Statistics, Stanford University, Stanford, CA 94305, USA.; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA., Candès E; Department of Statistics, Stanford University, Stanford, CA 94305, USA.; Department of Mathematics, Stanford University, Stanford, CA 94305, USA., He Z; Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.; Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, 94305, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2025 Jun 07. Date of Electronic Publication: 2025 Jun 07.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2025.06.03.657761