Journal of Vaccines & Vaccination

Development of new machine learning and mathematical modeling method for understanding of biological functions with big data

7^th Middle East - Global Summit and Expo on Vaccines & Vaccination

September 28-29, 2015 Dubai, UAE

Kazuo Ishii

Tokyo University of Agriculture and Technology, Japan

Keynote: J Vaccines Vaccin

Abstract:

Emergence of the next generation sequencing technology and its application to clinical and biological research opened the door of the era of genomic big data. Today, realizing the personal medicine with big data is fascinating topic in the current clinical research. To discover the new knowledge from genomic big data, the novel statistical computing and mathematical modeling method for understanding of biological functions with genomic big data named PMC machine learning, which is based on Monte-Carlo methods and parallel computing, will be discussed. Monte-Carlo method is a random sampling method such as bootstrap, jackknife and Marcov-chain Monte-Carlo methods. Parallel computing was implemented by (1) high performance computing (HPC) with many core CPUs, (2) cloud computing based on a computer cluster, (3) GPGPU (General-purpose computing on graphics processing units) and (4) a parallel FPGA cluster. In this presentation, some applications of PMC machine learning for cancer vaccination and immunotherapy will be discussed.

Biography :

Kazuo Ishii, PhD, is the Professor of Genomic Science of the Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Japan, in 2011 and he has dedicated to the Human Resource Development Program in Agricultural Genomic Sciences in Tokyo University of Agriculture and Technology. His main interest lies in statistical computing and mathematical modeling for understanding of biological functions with big data.

Email: kishii@cc.tuat.ac.jp