Kim Kibeom, Kim Juseong, Kim Minwook, Lee Hyewon, Song Giltae
Division of Artificial Intelligence, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, South Korea.
Department of Cardiology, Medical Research Institute, Pusan National University Hospital, 179 Gudeok-ro, Busan 49241, South Korea.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf019.
Identifying therapeutic genes is crucial for developing treatments targeting genetic causes of diseases, but experimental trials are costly and time-consuming. Although many deep learning approaches aim to identify biomarker genes, predicting therapeutic target genes remains challenging due to the limited number of known targets. To address this, we propose HIT (Hypergraph Interaction Transformer), a deep hypergraph representation learning model that identifies a gene's therapeutic potential, biomarker status, or lack of association with diseases. HIT uses hypergraph structures of genes, ontologies, diseases, and phenotypes, employing attention-based learning to capture complex relationships. Experiments demonstrate HIT's state-of-the-art performance, explainability, and ability to identify novel therapeutic targets.
识别治疗性基因对于开发针对疾病遗传病因的治疗方法至关重要，但实验性试验成本高昂且耗时。尽管许多深度学习方法旨在识别生物标志物基因，但由于已知靶点数量有限，预测治疗靶点基因仍然具有挑战性。为了解决这个问题，我们提出了HIT（超图交互变压器），这是一种深度超图表示学习模型，可识别基因的治疗潜力、生物标志物状态或与疾病的无关性。HIT使用基因、本体、疾病和表型的超图结构，采用基于注意力的学习来捕捉复杂关系。实验证明了HIT的先进性能、可解释性以及识别新治疗靶点的能力。
