报告题目：Advanced Machine Learning in Genetic Data
　　时间：2019年1月7日（周一）上午10:00
　　地点：计算所948会议室
　　报告人：Wei Ding
　　摘要：In this talk, I will discuss two new biological inspired neural networks that integrate model-based approach (existing domain knowledge) with data driven function-based approach (machine learning algorithms) to realize their collective benefits and with their applications to Cancer Subtyping and Clinical Trials, using gene mutation and gene expression data respectively. The Reinforced and Informed Network-based Clustering (RINC), for finding unknown groups of similar data
　　objects in sparse and largely non-overlapping feature space where a network structure among features can be observed. The RINC learning algorithm efficiently clusters sparse data through integrated smoothing and sparse auto-encoder learning. The second biological network-based regularized artificial neural network model is for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. Empirical results demonstrate that our advanced machine learning algorithms achieve improved accuracy and renders physically meaningful experimental results.
　　报告人简介：Wei Ding received her Ph.D. degree in Computer Science from the University of Houston in 2008. She is an Associate Professor of Computer Science in the University of Massachusetts Boston. Her research interests include data mining, machine learning, artificial intelligence, computational semantics, and with applications to health sciences, astronomy, geosciences, and environmental sciences. She has published more than 130 referred research papers, one book, and has three patents. She is an Associate Editor of the ACM Transaction on Knowledge Discovery from Data (TKDD) and Knowledge and Information Systems (KAIS). She served as an editorial board member of the Journal of Information System Education (JISE), the Journal of Big Data, and the Social Network Analysis and Mining Journal. She is the recipient of the Best Paper Award at the 2011 IEEE International Conference on Tools with Artificial Intelligence (ICTAI), the Best Paper Award at the 2010 IEEE International Conference on Cognitive Informatics (ICCI), the Best Poster Presentation award at the 2008 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPAITAL GIS), and the 2018 Outstanding Alumni Award and the Best PhD Work Award between 2007 and 2010 from the University of Houston. Her research projects have been sponsored by NSF, NIH, NASA, and DOE. She is an IEEE senior member and an ACM senior member.