整合多源基因相互作用网络和omics数据与图表注意网络,以识别新型疾病基因
Kaiyuan Yang1, Jiabei Cheng1, Shenghao Cao1
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
Bioinformatics (Oxford, England)
|April 23, 2025
概括
我们开发了DGP-AMIO,这是一种使用图表注意网络的计算方法,通过整合多omics和基因相互作用数据来识别新型疾病基因. 这种方法在预测疾病相关基因方面显著优于现有的方法.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因发现对于了解疾病的发病因子和开发向疗法至关重要.
- 对所有基因的生物验证是昂贵和困难的.
- 鉴定新型疾病基因仍然是生物医学研究中的一个重大挑战.
研究的目的:
- 提出一种计算方法,DGP-AMIO,用于对未知的基因进行排名,并识别潜在的新型疾病基因.
- 整合多omics数据和基因相互作用网络,以改善疾病基因预测.
- 为指导预测疾病基因的实验验证提供一个有效的工具.
主要方法:
- 开发了DGP-AMIO,一种基于图表注意力网络 (GAT) 的方法.
- 来自多个来源的综合多omics数据和基因相互作用网络.
- 利用 triGAT,一种基于 GAT 的新型方法用于定向基因网络.
主要成果:
- DGP-AMIO在20个疾病数据集上表现出卓越的性能,平均AUROC和AUPR超过0.9.
- 对最高预测基因的丰富分析证实了与已知的疾病和途径的关联.
- 该方法有效地在指导基因网络中识别疾病基因.
结论:
- DGP-AMIO是一种强大的计算工具,用于识别新型疾病基因.
- 多基因和基因网络的整合提高了预测的准确性.
- 该方法可以显著帮助优先考虑实验验证的基因.
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