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张晓飞:Imputing Dropout Events in Single Cell RNA Sequencing Data Using Machine Learning(时间3.12)

发布日期:2021-03-08  作者:刘敏  浏览数:

报告人:张晓飞 华中师范大学教授

报告时间:3月12日10:00

报告地点:腾讯ID:908 811 526

主办单位:数学与统计学院

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  报告摘要:Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution, paving a new way for understanding of the function of an individual cell in the context of its microenvironment. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. In this talk, we will introduce our recent studies for recovering gene expression for scRNA-seq by imputing the dropout events. Several experiments will be conducted to show the competitive performance of the proposed methods.

  张晓飞,博士,华中师范大学数学与统计学学院教授,博士研究生导师。主要从事基于机器学习方法的大规模生物医学组学数据挖掘研究。现主持国家自然科学基金面上项目1项,参与国家重点研发计划“精准医学研究”重点专项1项,参与国家自然科学基金重点项目1项。国家自然科学基金信息学部讯评审专家。已在Bioinformatics、 Briefings in Bioinformatics、IEEE Transactions on Cybernetics、 IEEE Transactions on Image Processing、IEEE/ACM Transactions on Computational Biology and Bioinformatics等期刊发表学术论文40余篇,开发应用软件包20余个。

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