Work

Attention-aware Rare Disease Diagnosis via Graph Attention Neural Network

Year: 2024

Type: conference-paper

Abstract: Monogenic genetic diseases, particularly rare ones, pose a significant challenge to human health.Although next-generation sequencing technology has greatly improved the diagnostic rate of these diseases, the process remains time-consuming and labor-intensive.Phenotype-driven diagnostic tools are evolving rapidly and supporting clinical practitioners.In this study, we proposed a diagnostic model based on a graph attention network.This model uses attention mechanisms to uncover potential relationships between genes and phenotypes, and generates a ranked list of candidate genes for identifying causal gene.Our model was tested on three standard datasets and demonstrated an excellent performance.

Source: International Conference on Computational Biology and Bioinformatics (ICCBB)

DOI: 10.1145/3715020.3715060

Authors: Xin Yang, Dongmei He, Buchao Zhan, Qingcheng Zhang, Shankai Yan

Institution: Hainan University

Language: English

Topic: Genomics and Rare Diseases

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Natural Science Foundation of China, Hainan University