Hierarchical similarity network fusion for discovering cancer subtypes

Shuhui Liu, Xuequn Shang

科研成果: 书/报告/会议事项章节会议稿件同行评审

9 引用 (Scopus)

摘要

Recent breakthroughs in biologic sequencing technologies have cost-effectively yielded diverse types of observations. Integrative analysis of multiple platform cancer data, which is capable of revealing intrinsic characteristics of a biological process, has become an attractive research route on cancer subtypes discovery. Most machine learning based methods need represent each input data in unified space, losing certain important features or resulting in various noises in some data types. Furthermore, many network based data integration methods treat each type data independently, leading to a lot of inconsistent conclusions. Subsequently, similarity network fusion (SNF) was developed to deal with such questions. However, Euclidean distance metrics employed in SNF suffers curse of dimensionality and thus gives rise to poor results. To this end, we propose a new integrated method, dubbed hierarchical similarity network (HSNF), to learn a fused discriminating patient similarity network. HSNF randomly samples sub-features from different input data to construct multiple input similarity matrixes used as a basic of fusion so that diverse similarity matrixes are generated by multiple random sampling. Then we design a hierarchical fusion framework to make full use of the complementariness of diverse similarity networks from different feature modalities. Finally, based on the final fused similarity matrix, spectral clustering was used to discover cancer subtypes. Experimental results on five public cancer datasets manifest that HSNF can discover significantly different subtypes and can consistently outperform the-state-of-the-art in terms of silhouette, and p-value of survival analysis.

源语言英语
主期刊名Bioinformatics Research and Applications - 14th International Symposium, ISBRA 2018, Proceedings
编辑Fa Zhang, Shihua Zhang, Zhipeng Cai, Pavel Skums
出版商Springer Verlag
125-136
页数12
ISBN(印刷版)9783319949673
DOI
出版状态已出版 - 2018
活动14th International Symposium on Bioinformatics Research and Applications, ISBRA 2018 - Beijing, 中国
期限: 8 6月 201811 6月 2018

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10847 LNBI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议14th International Symposium on Bioinformatics Research and Applications, ISBRA 2018
国家/地区中国
Beijing
时期8/06/1811/06/18

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