主 题：Baysian Neutro Networks for selection of anticancer drug response genes
Recent advances in high-throughput biotechnologies have provided an unprecedented opportunity for biomarker discovery, which, from a statistical point of view, can be cast as a variable selection problem. This problem is challenging due to the high-dimensional and non-linear nature of omics data and, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo (MCMC) algorithm, on the OpenMP platform which provides a linear speedup for the simulation. The numerical results indicate that the proposed method can execute very fast on a multicore computer and work very well for identification of relevant variables for general high-dimensional nonlinear systems. The proposed method is successfully applied to selection of anticancer drug response genes for the drug sensitivity data collected in the cancer cell line encyclopedia (CCLE) study.
李启寨 中国科学院数学与系统科学研究院 研究员，主要研究方向：生物医学统计，统计遗传学，临床试验等。在Nature Genetics, American Journal of Human Genetics, JASA等杂志发表SCI论文72篇，被他人SCI论文引用1300余次。多项研究成果被美国科学院院士等研究者用到30多种复杂疾病的关联研究中。目前担任Scientific Reports (SCI)， PLOS One (SCI), Journal of Systems Science and Complexity (SCI), Journal of Applied Statistics (SCI)的杂志的编委。曾获美国国家癌症研究所DCEG突出科研成果奖和优秀论文奖，中国工业与应用数学学会优秀青年学者奖，国际统计研究所推选会员(Elected Member)等。