Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 24, Issue 2, Pages 172-182Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2015.0206
Keywords
breast cancer metastasis; GeneRank algorithm; PPI information
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Funding
- National Basic Research Program of China [2012CB910400]
- National Natural Science Foundation of China [81330059]
- National Major Scientific and Technological Special Project for Significant New Drugs Development'' [2013ZX 09507001]
- National Science and Technology Support Plan Project [2015BAH12F01]
- Science and Technology Commission of Shanghai Municipality [14DZ2270100]
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The selection of relevant genes for breast cancer metastasis is critical for the treatment and prognosis of cancer patients. Although much effort has been devoted to the gene selection procedures by use of different statistical analysis methods or computational techniques, the interpretation of the variables in the resulting survival models has been limited so far. This article proposes a new Random Forest (RF)-based algorithm to identify important variables highly related with breast cancer metastasis, which is based on the important scores of two variable selection algorithms, including the mean decrease Gini (MDG) criteria of Random Forest and the GeneRank algorithm with protein-protein interaction (PPI) information. The new gene selection algorithm can be called PPIRF. The improved prediction accuracy fully illustrated the reliability and high interpretability of gene list selected by the PPIRF approach.
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