Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities.

Tomohiro Ando, Seiya Imoto, Satoru Miyano

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

One important application of microarray gene expression data is to study the relationship between the clinical phenotype of cancer patients and gene expression profiles on the whole-genome scale. The clinical phenotype includes several different types of cancers, survival times, relapse times, drug responses and so on. Under the situation that the subtypes of cancer have not been previously identified or known to exist, we develop a new kernel mixture modeling method that performs simultaneously identification of the subtype of cancer, prediction of the probabilities of both cancer type and patient's survival, and detection of a set of marker genes on which to base a diagnosis. The proposed method is successfully performed on real data analysis and simulation studies.

Original languageEnglish
Pages (from-to)201-210
Number of pages10
JournalGenome informatics. International Conference on Genome Informatics
Volume15
Issue number2
Publication statusPublished - 2004

ASJC Scopus subject areas

  • Medicine(all)

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