科研之友
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摘要
In the past years, there has been a growing interest in methods that incorporate network information into classification algorithms for bio-marker 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.
- 出版日期2014-5-1
