报 告 人：李启寨研究员（中国科学院）

报告题目：前馈贝叶斯神经网络及其在抗癌药物响应中的应用

报告时间：11月23日(周四)下午3:00-4:00

报告地点：知新楼B-1238

报告摘要：

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 rst 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 identi cation 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.

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