演讲摘要：Cancer has been characterized as a heterogeneous disease, and the classification of cancer subtypes has become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients and provide clinical decision support for clinicians. With the advance of machine learning in the last decade, many researchers employ machine learning to tackle the cancer classification problem. However, collection of large amounts of annotated data is time-consuming and expensive and may not be realistic in real-world activities. Meta-learning is a new paradigm which utilizes prior knowledge learned from related tasks and generalizes to new tasks of limited supervised experience, and it has been applied in many fields to tackle scarce annotated data problem, such as cancer, drug discovery, etc. In this talk, I will discuss the potential of applying the meta-learning algorithms to analyze The Cancer Genome Atlas (TCGA) cancer patient data and demonstrates the effectiveness and superiority of the meta-learning methods for cancer prediction.
讲者简介：Aidong Zhang is a William Wulf Faculty Fellow and Professor of Computer Science and Biomedical Engineering in the School of Engineering and Applied Sciences at University of Virginia. She is also affiliated with the Data Science Institute at University of Virginia. Her research interests focus on machine learning, bioinformatics and health informatics. Dr. Zhang currently serves as the Editor-in-Chief of the IEEE Transactions on Computational Biology and Bioinformatics (TCBB). She served as the founding Chair of ACM Special Interest Group on Bioinformatics, Computational Biology and Biomedical Informatics during 2011-2015 and is currently the Chair of its advisory board. She is also the founding and steering chair of ACM international conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB). Dr. Zhang is a fellow of ACM and IEEE.
第五届中国计算机学会生物信息学会议
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