[主旨报告]Recent Advances in Deep Neural Networks for Mining Biological Sequence Motifs

Recent Advances in Deep Neural Networks for Mining Biological Sequence Motifs
编号:175 访问权限:仅限参会人 更新:2022-07-14 09:49:55 浏览:425次 主旨报告

报告开始:2022年07月25日 08:25 (Asia/Shanghai)

报告时间:25min

所在会议:[P] 全体会议 » [P-3] 闭幕式及主旨报告3

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摘要
Transcription factor/Translation factor (TF) play a central role in gene regulation. Knowing the binding specificities of TFs is essential for developing models of the regulatory processes in biological systems and for deciphering the mechanism of gene expression. In this talk, I will first present the fundamental issue for motif prediction of biological sequences, then systematically present motif prediction of biological sequences in combination with the popular emerging technology “Deep Neural Networks”. Firstly, several classical models for deep neural network and the research status of biological sequence motif prediction will be briefly introduced, and the existing shortcomings of deep-learning based motif prediction is discussed, some motif prediction methods including high-order convolutional neural network architecture, weakly-supervised convolutional neural network architecture, deep-learning based sequence + shape framework and bidirectional recurrent neural network for DNA motif prediction are briefly overviewed. Secondly, some latest results are importantly presented. Finally, some new research problems in this aspect will be pointed out and over-reviewed.
 
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报告人
黄德双
同济大学

黄德双,东方理工大学教授,同济大学教授,博士生导师, 中国科学技术大学兼职教授与博士生导师, IEEE Fellow,国际模式识别学会(IAPR) Fellow,亚太人工智能学会(AAIA) Fellow,纽约科学院Active Member,2000年度中科院“百人计划”入选者。国家新一代人工智能重大项目首席科学家(主持人),国家自然科学基金委第十四届专家评审组成员,国家杰青、优青函评与会评专家;国家科技部重点研发计划、国家奖会评与函评专家。中国生物信息学学会(筹)生物医学数据挖掘与计算专委会主任(2021-),中国计算机学会生物信息学专业委员会副主任委员(2019-)。获教育部自然科学一等奖一项(排名第一),安徽省自然科学一等奖一项(排名第一),获中国人工智能学会吴文俊科技进步一等奖一项(排名第一),2014-2021年度爱思唯尔(Elsevier)Scopus高被引学者(计算机科学卷)。Google Scholar引用20080余次,H因子76。
 

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