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

ABSTRACT: Histological images offer a wealth of data. Mining these data holds significant potential for enhancing disease diagnosis and prognosis, though challenges remain, especially in non-cancer contexts. In this study, we developed a statistical framework that links raw histological images and their derived features to the genotype, transcriptome, and chronological age of the samples. We first demonstrated an association between image features and genotypes, identifying 906 image quantitative trait loci (imageQTLs) significantly associated with image features. Next, we identified differentially expressed (DE) genes by stratifying samples into image-similar groups based on image features and performing DE comparisons between groups. Additionally, we developed a deep-learning model that accurately

SUBMITTER: Meng R 

PROVIDER: S-EPMC12439936 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature