Computational Personalized Medicine in Cancer Research in the -Omics Data Era
Abstract
Omics data (e.g., genomics, transcriptomics, proteomics, epigenomics, etc . . . ) generated from high-throughput
next-generation sequencers in the big human genome, and
cancer genome projects have changed the way to study
personalized medicine. In the future, personalized medicine
will not be limited to diagnosis and treatment based on a
few known disease-associated mutations on some genes, but
will rely on whole molecular characteristics of patients by
integrating their –omics data. In this study, we draw a big
picture of personalized medicine research in cancer research
of the –omics data era, including –omics databases, challenges
of data fusion to solve two major problems in personalized
medicine, i.e., personalized diagnosis and treatment. These
problems are approached as patient stratification and drug
response prediction based on the –omics data by computational methods.