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Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning
2014
PLoS ONE
The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified
doi:10.1371/journal.pone.0086309
pmid:24497942
pmcid:PMC3908883
fatcat:hbraslpwfnehjki5fwqra6wkji