4.7 Article

An Ontology-Independent Representation Learning for Similar Disease Detection Based on Multi-Layer Similarity Network

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2019.2941475

关键词

disease similarity; disease information network; representation learning; multi-layer similarity network

资金

  1. National Natural Science Foundation of China [61572332, 81473446]
  2. Fundamental Research Funds for the Central Universities [2016SCU04A22]
  3. China Postdoctoral Science Foundation [2016T90850]
  4. COST ACTION Open Multiscale Systems Medicine [CA15120]

向作者/读者索取更多资源

Identifying similar diseases is important for understanding disease etiology and pathogenesis in biomedicine. The study proposed a novel framework called RADAR which integrates disease similarity networks from multiple data sources to comprehensively evaluate disease similarities, demonstrating effective detection of similar diseases.
To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently, most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then, a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据