Effective Subject Representation based on Multi-omics Disease Networks using Graph Embedding.
Effective Subject Representation based on Multi-omics Disease Networks using Graph Embedding.
复制标题
DOI：
10.1109/bibm55620.2022.9995707
复制
发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
Banaei-Kashani, Farnoush
中科院分区：
文献类型：
--
作者：
Hussein, Sundous;Vu, Thao;Lange, Leslie;Bowler, Russell P;Kechris, Katerina J;Banaei-Kashani, Farnoush
生物系统复杂行为的研究越来越依赖于进化网络建模。特别是，多组学网络捕捉生物分子如蛋白质和代谢物之间的相互作用，为预测这些生物分子与复杂疾病的各种表型性状之间的关系提供了基础。在本文中，我们介绍了一个综合框架，给出了一个多组学网络代表一个队列的主题，学习网络节点的表达表示，并结合学习的节点表示与生物学个人资料的主题丰富的代表。通过使用真实世界的多组学网络进行广泛的实证评估，我们表明，我们提出的框架在主题表示准确性方面显着优于现有和基线方法，特别是当代表队列的多组学网络稀疏且结构化，因此信息量更大时。
The study of complex behavior of biological systems has become increasingly dependent on evolutionary network modeling. In particular, multi-omics networks capture interactions between biomolecules such as proteins and metabolites, providing a basis for predicting relationships between such biomolecules and various phenotypic traits of complex diseases. In this paper, we introduce an integrative framework that given a multi-omics network representing a cohort of subjects, learns expressive representations for network nodes, and combines the learned nodes representations with the biological profiles of individual subjects for enriched representation of the subjects. With extensive empirical evaluation using real-world multi-omics networks, we show that our proposed framework significantly outperforms existing and baseline methods in terms of subject representation accuracy, particularly when the multi-omics network representing the cohort is sparse and structured and therefore, more informative.