时间：2019.3.21（周四）上午10:00-11:00
地点：生命学院221会议室
报告人： 周小波 教授
德州大学休斯顿医学中心 终身正教授
计算系统医学中心主任
周博士在大数据挖掘，云计算，人工智能，机器学习，生物信息学，精准医疗，系统生物学，生物医学成像，图像处理和模式识别，药物靶标预测, 测序数据分析,磷蛋白质组学的信号通路研究, 细胞与细胞的相互作用建模, 癌症干细胞小环境建模, 免疫系统建模，再生医学系统建模研究，計算机辅助的手術治疗，以及系统建模指导下的药物治疗系统等有着多年的研究经验。
他发表了200多篇期刊论文，包括顶尖期刊上如Science，Nature Series, Genome Biology, Nucleic Acids Research，Annals of Internal Medicine, Cancer Research，Biomaterials，Bioinformatics等，总影响因子超过了800。论文在过去的五年被引用次数超过了5000次。另外出版了10本书籍章节和2本专著。自2005年以来，周博士从美国NIH获得了超过3000万美元的研究经费。
报告内容: Alternative splicing (AS) is one of the most important pre-mRNA processing to increase the diversity of transcriptome and proteome in tissue- and development-dependent manners. AS has critical roles in normal development and human diseases. Its regulation has long been thought to involve genetic elements, such as cis-acting RNA elements and trans-regulatory splicing factors, which have been assembled into a splicing genetic code. However, these genetic controls are far from sufficient to explain the faithful regulation of AS. We and others have found that AS also undergoes controls from epigenetic mechanisms, especially the histone modifications (HMs), due to its co-transcriptional occurrence. HMs determine not only what parts of the genome are expressed, but also how they are spliced. These emerging findings have inspired new avenues in studying AS regulation and raised a more complex regulatory model. Recently we first developed a novel deep learning approach to build the splicing (epi)genetic code atlas (Nucleic Acids Research, 2017), and then developed a probabilistic model, epiSMINT to identify the most dynamic splicing module transitions and important AS genes during lineage commitment, promising the functional interpretations (Genome Biology, 2018). Finally, I will briefly introduce the multiscale resolution and deep network approach for deconvolving different cell types in bulk tumor using single-cell sequencing Data (Nature Communications, 2019). In this talk, I will demo both new bioinformatics tools and novel biological insights.
