Big data analysis of cancer
Thanks to high-throughput experimental technologies, a massive amount of research data on cancer genomics have been accumulated in public databases. It is expected that public data will continue to explosively increase due to continued introduction of high-throughput technologies. However, a shortage of bioinformaticians causes “data indigestion”, which prevents us from establishing systematic strategies and elementary technologies that can utilize medical big data for precision medicine (personalized genome medicine) in the near future.
In this project, we conduct integrated analysis of genome data and other omics data (e.g., epigenome, transcriptome, and proteome data) with clinical data, and develop bioinformatics technologies that can yield new discoveries unattainable with conventional single-omics layer analysis due to small sample sizes. This includes multiple-omics layer associations in a cancer gene, associations between multiple types of cancer, associations with clinical data, and associations between different genes. Using our developed technologies, we will create new knowledge on disease classification, diagnosis, treatment, and prevention that will be applied to clinical practice（Reference：Pharma Medica、2016, 34, 45-51）.