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Article

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.

Bioinformatics (Oxford, England) - 1 Nov 2023

Bi Xuehua, Liang Weiyang, Zhao Qichang, Wang Jianxin

Abstract excerpt

MOTIVATION: Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories. RESULTS: To address the problem of labeled-data scarcity, we...

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Identifiers

PMID: 37941450
DOI: 10.1093/bioinformatics/btad662
PMCID: PMC10666204

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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