DiSMVC:一个多视图图协作学习框架,用于测量疾病相似性
Hang Wei1, Lin Gao1, Shuai Wu1
1School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
Bioinformatics (Oxford, England)
|May 8, 2024
概括
我们开发了DiSMVC,这是一种新的计算方法,通过整合多分子调节来测量疾病相似性. 这种方法增强了对疾病关联的理解,并有助于生物标志物的发现.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 了解疾病关联对于识别生物标志物和药物点至关重要.
- 现有的疾病相似性的计算方法缺乏生物解释性和效率,因为对多分子调节的考虑有限.
研究的目的:
- 提出DiSMVC,一种用于测量疾病相似性的新计算方法.
- 提高疾病关联模式捕获的生物解释性和效率.
主要方法:
- DiSMVC使用了一个监督图表协作框架.
- 它通过交叉视图对比学习整合了基因和miRNA关联,用于疾病表示.
- 疾病相似性是使用关联模式联合学习与表型数据计算的.
主要成果:
- DiSMVC有效地提取疾病对的歧视性特征.
- 该方法在预测疾病关联方面优于现有的最先进的方法.
- 实验结果证明了DiSMVC对分子解释性的潜力.
结论:
- DiSMVC为预测疾病关联提供了一种有前途的方法.
- 与以前的计算工具相比,该方法提供了增强的分子解释性.
- DiSMVC有助于更深入地了解疾病的病理机制.
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