Bonn-Aachen International Center for IT (B-IT), University of Bonn, Dahlmannstr. 2, 53113 Bonn, Germany.
Bioinformatics. 2014 May 1;30(9):1325-6. doi: 10.1093/bioinformatics/btu025. Epub 2014 Jan 17.
In the past years, there has been a growing interest in methods that incorporate network information into classification algorithms for biomarker signature discovery in personalized medicine. The general hope is that this way the typical low reproducibility of signatures, together with the difficulty to link them to biological knowledge, can be addressed. Complementary to these efforts, there is an increasing interest in integrating different data entities (e.g. gene and miRNA expressions) into comprehensive models. To our knowledge, R-package netClass is the first software that addresses both, network and data integration. Besides several published approaches for network integration, it specifically contains our recently published STSVM method, which allows for additional integration of gene and miRNA expression data into one predictive classifier.
在过去几年中，人们对将网络信息纳入分类算法以发现个性化医疗中生物标志物特征的方法越来越感兴趣。人们普遍希望，通过这种方式，可以解决特征的典型低再现性问题，以及将其与生物学知识联系起来的困难。除了这些努力之外，人们越来越有兴趣将不同的数据实体（例如基因和 miRNA 表达）整合到综合模型中。据我们所知，R 包 netClass 是第一个同时解决网络和数据集成问题的软件。除了几种已发布的网络集成方法外，它还特别包含了我们最近发布的 STSVM 方法，该方法允许将基因和 miRNA 表达数据额外集成到一个预测分类器中。
