报告题目：基于复杂网络分析和深度学习的疾病基因预测
报告华体会网页版登陆入口：2019年4月12日下午3点
报告地点：校本部计算机楼308会议室
报告人：罗平博士
摘要：复杂疾病通常是由一组基因的变异所引起的，我们称这些基因为疾病基因。识别疾病基因可以帮助我们了解疾病形成的原理，这对早期诊断疾病以及药物的研发都有很大的帮助。因为使用生物实验来识别疾病基因需要耗费大量的华体会网页版登陆入口和资金，所以科研工作者提出了很多算法，希望过计算机来预测疾病基因。这些算法的预测结果可以帮助生物化学家优化他们的实验，从而加速疾病基因的识别。目前，多种多样的生物数据都可以被用来预测疾病基因，如何更好的融合这些数据是准确预测疾病基因的关键。在我们的课题组，网络分析的方法和深度学习被结合起来，完成对不同种类数据的融合，从而实现对疾病基因的准确预测。在这次报告中，我将介绍我在博士期间的几项有关疾病基因预测的研究。其中一部分研究注重于基于深度学习的数据融合，另一部分研究则是侧重于临床基因表达数据的分析。
个人简介：2010年毕业于湖南大学计算机科学与技术专业，2015年于北京理工大学生物医学工程专业获得硕士学位，目前在加拿大萨斯喀彻温大学攻读博士学位。他的主要研究领域有：疾病基因预测、深度学习在生物信息学方面的应用、复杂网络分析、多组学数据的分析。
学术兼职：IEEETNNLS，Neurocomputing等
Title: Disease gene prediction based on complex network analysis and deep learning
Time: Apr 12, 2019, 3 pm
Location: Room 308, Computer Building
Speaker: Dr. Ping Luo
Abstract:Complexdiseases are caused by the malfunction of a group of genes (known as disease genes) and identifying them canhelp us understand the mechanism of diseases, which has many applications such as early diagnosis and drug development. Since experimental techniques for identifying disease gene are time-consuming and expensive, many computational algorithms have been developed to help scientists optimize the in-depth experimental validation and accelerate the identification of true disease genes.Currently, various types of data can be used to predict disease genes, and properly integrating them is the key issue for accurate prediction. In our group, network analysis algorithms and deep learning models are combined to fuse different types of data and enhance the prediction. In this talk, I will present a few studies of my Ph.D. program in disease genes prediction. Some of themuse deep learning models to fuse the data, while others focus on clinical expression data and achieve the prediction by classic machine learning models.
About the speaker: Hereceived his bachelor's degree in Computer Science and Technology from Hunan University in 2010 and master's degree in Biomedical Engineering from Beijing Institute of Technology in 2015. Currently, he is working toward the Ph.D. degree in the Division of Biomedical Engineering at the University of Saskatchewan, Saskatoon, Canada. His research interests include disease gene prediction, deep learning in Bioinformatics, biomolecular network analytics, and multi-omics data analytics.
Academic job:reviewer ofIEEE TNNLS,Neurocomputingetc.
