Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.

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Bibliographic Details
Title: Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.
Authors: Meng R; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA., Zhu W; Department of Computer Sciences, Yale University, New Haven, CT 06520, USA., Cameron CJ; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06520, USA., Ni P; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA., Zhou X; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA., Ulammandakh T; Department of Computer Sciences, Yale University, New Haven, CT 06520, USA., Gerstein MB; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.; Department of Computer Sciences, Yale University, New Haven, CT 06520, USA.; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA.; Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2025 Sep 09. Date of Electronic Publication: 2025 Sep 09.
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:10.1101/2025.09.04.674328