Ge Gao (高歌)

Research Interests (研究兴趣)

As biology is increasingly turning into a data-rich science, massive data generated by high-throughput technologies pose both opportunities and serious challenges. My team is interested primarily in developing novel computational techniques to analyze, integrate and visualize high-throughput biological data effectively and efficiently, with application to decipher the function and evolution of gene regulatory system. Drawing on my background in computational sciences, my team specialize in large-scale data mining, using a combination of statistical learning, high-performance computing, and data visualizing.

随着以深度测序为代表的高通量生物技术在生命科学领域的广泛应用，各种生物学大数据以指数增长大量涌现。这些数据之中蕴藏着大量的宝藏，即生物学的新规律、新发现。但是，这些海量的、指数增长的、并且高噪声的生物数据也带来了巨大的数据分析技术上的挑战。

课题组以生物信息学分析技术、方法与平台开发为基础，通过综合运用大数据与统计学习(statistical learning)等计算方法，整合高通量遗传学与功能基因组学数据，探索新表达调控因子的功能与演化及其对生物体新性状和新功能的贡献。目前课题组主要研究方向包括1) 非编码RNA对干细胞命运决定过程的调控、与2) 基因组中适应性基因获得/丢失对调控网络演化的影响。

Professional Service and Honors (专业荣誉)

Member of the Executive Committee and China Liaison, Asia Pacific BioInformatics Network (APBioNET)，2008-

首届绿叶生物医药杰出青年学者奖 , 2010

APBioNet Service Award , 2010

首届国家青年拔尖人才 , 2012

北京大学教学优秀奖，2014