Cancer driver mutation prediction through Bayesian integration of multi-omic data

Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel...

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Dettagli Bibliografici
Pubblicato in:PLoS One
Autori principali: Wang, Zixing, Ng, Kwok-Shing, Chen, Tenghui, Kim, Tae-Beom, Wang, Fang, Shaw, Kenna, Scott, Kenneth L., Meric-Bernstam, Funda, Mills, Gordon B., Chen, Ken
Natura: Artigo
Lingua:Inglês
Pubblicazione: Public Library of Science 2018
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Research Article
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC5940219/
https://ncbi.nlm.nih.gov/pubmed/29738578
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0196939
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