GenePheno: Interpretable Gene Knockout-Induced Phenotype Abnormality Prediction from Gene Sequences

• URL: http://arxiv.org/abs/2511.09512v2
• Date: Fri, 14 Nov 2025 20:42:29 GMT
• Title: GenePheno: Interpretable Gene Knockout-Induced Phenotype Abnormality Prediction from Gene Sequences
• Authors: Jingquan Yan, Yuwei Miao, Lei Yu, Yuzhi Guo, Xue Xiao, Lin Xu, Junzhou Huang,
• Abstract summary: We introduce GenePheno, the first interpretable multi-label prediction framework that predicts knockout induced phenotypic abnormalities from gene sequences.<n>GenePheno employs a contrastive multi-label learning objective that captures inter-phenotype correlations, complemented by an exclusive regularization that enforces biological consistency.<n>GenePheno achieves state-of-the-art gene-centric $F_textmax$ and phenotype-centric AUC, and case studies demonstrate its ability to reveal gene functional mechanisms.
• Score: 23.32906953921921
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Exploring how genetic sequences shape phenotypes is a fundamental challenge in biology and a key step toward scalable, hypothesis-driven experimentation. The task is complicated by the large modality gap between sequences and phenotypes, as well as the pleiotropic nature of gene-phenotype relationships. Existing sequence-based efforts focus on the degree to which variants of specific genes alter a limited set of phenotypes, while general gene knockout induced phenotype abnormality prediction methods heavily rely on curated genetic information as inputs, which limits scalability and generalizability. As a result, the task of broadly predicting the presence of multiple phenotype abnormalities under gene knockout directly from gene sequences remains underexplored. We introduce GenePheno, the first interpretable multi-label prediction framework that predicts knockout induced phenotypic abnormalities from gene sequences. GenePheno employs a contrastive multi-label learning objective that captures inter-phenotype correlations, complemented by an exclusive regularization that enforces biological consistency. It further incorporates a gene function bottleneck layer, offering human interpretable concepts that reflect functional mechanisms behind phenotype formation. To support progress in this area, we curate four datasets with canonical gene sequences as input and multi-label phenotypic abnormalities induced by gene knockouts as targets. Across these datasets, GenePheno achieves state-of-the-art gene-centric $F_{\text{max}}$ and phenotype-centric AUC, and case studies demonstrate its ability to reveal gene functional mechanisms.

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