Semi-supervised learning improves gene expression-based prediction of cancer recurrence

Motivation: Gene expression profiling has shown great potential in outcome prediction for different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining robust and accurate classifiers. Traditional supervised learning techniques can only work with labeled data. Consequ...

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Veröffentlicht in:	Bioinformatics 2011-11, Vol.27 (21), p.3017-3023
Hauptverfasser:	Shi, Mingguang, Zhang, Bing
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Bioinformatics Biological and medical sciences Breast Neoplasms - genetics Colonic Neoplasms - genetics Disease-Free Survival Female Fundamental and applied biological sciences. Psychology Gene Expression Profiling General aspects Humans Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Neoplasm Recurrence, Local - genetics Original Papers Support Vector Machine
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