Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities.
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.
Ando
Tomohiro
T
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. ando@ims.u-tokyo.ac.jp
Imoto
Seiya
S
Miyano
Satoru
S
D001185:Artificial Intelligence; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000401:mortality*; D016393:Lymphoma, B-Cell / Q000235:genetics / Q000401:mortality; D016403:Lymphoma, Large B-Cell, Diffuse / Q000235:genetics / Q000401:mortality; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010641:Phenotype; D016016:Proportional Hazards Models; D016019:Survival Analysis