Zhang Yong, Hu Xiaohua, Jiang Xingpeng
IEEE/ACM Trans Comput Biol Bioinform. 2017 Mar-Apr;14(2):264-271. doi: 10.1109/TCBB.2015.2474387. Epub 2015 Oct 26.
Microbiome datasets are often comprised of different representations or views which provide complementary information, such as genes, functions, and taxonomic assignments. Integration of multi-view information for clustering microbiome samples could create a comprehensive view of a given microbiome study. Similarity network fusion (SNF) can efficiently integrate similarities built from each view of data into a unique network that represents the full spectrum of the underlying data. Based on this method, we develop a Robust Similarity Network Fusion (RSNF) approach which combines the strength of random forest and the advantage of SNF at data aggregation. The experimental results indicate the strength of the proposed strategy. The method substantially improves the clustering performance significantly comparing to several state-of-the-art methods in several datasets.
微生物组数据集通常由不同的表示形式或视图组成，这些表示形式或视图提供互补信息，例如基因、功能和分类学归属。整合多视图信息以对微生物组样本进行聚类，可以创建给定微生物组研究的全面视图。相似性网络融合（SNF）可以有效地将从数据的每个视图构建的相似性整合到一个独特的网络中，该网络代表基础数据的全谱。基于此方法，我们开发了一种稳健相似性网络融合（RSNF）方法，该方法结合了随机森林的优势和SNF在数据聚合方面的优势。实验结果表明了所提出策略的优势。与几个数据集中的几种最先进方法相比，该方法显著提高了聚类性能。
