We are very happy that Shanfeng Zhu will give an invited talk in the eighth BioASQ workshop.
Title: "Recent advances in large-scale biomedical semantic indexing" (slides)
Speaker: Shanfeng Zhu, Fudan University
Abstract: With the rapid increase of biomedical articles, large-scale automatic semantic indexing has become increasingly important. For example, Medical Subject Headings (MeSHs) are used by the National Library of Medicine (NLM) to index almost all 30 million citations in MEDLINE. This greatly facilitates the applications of biomedical information retrieval and text mining. However, the automatic MeSH indexing still faces challenges as results of 1) a large number of labels (around 30000); 2) deep semantic information of biomedical text; and 3) limited information in title and abstract. The BioASQ challenge provides a realistic and practical benchmark to advance the design of effective algorithms for large-scale MeSH indexing. Over the last few years, we have developed a series of SOTA machine learning-based methods to address these challenges in large-scale MeSH indexing, such as MeSHLabeler, DeepMeSH, AttentionXML, FullMeSH, and BERTMeSH. Additionally, I will talk about the problem of large-scale biomedical semantic indexing in languages other than English.
Biosketch: Shanfeng Zhu is an Associate Professor at the Institute for Science and Technology for Brain-Inspired Intelligence, and the Shanghai Institute of Artificial Intelligence Algorithms at Fudan University. He is also a member of Shanghai Key Lab of Intelligent Information Processing, and Key Lab of Computational Neuroscience and Brain-Inspired Intelligence (MOE) at Fudan University. He received his Bachelor and Masters degrees in Computer Science at Wuhan University and Ph.D. in Computer Science at City University of Hong Kong in 1996, 1999 and 2003, respectively. Before joining Fudan University in 2008, he was a Postdoctoral Fellow at Bioinformatics Center, Kyoto University. He was a visiting scholar at UIUC (March 2013-March 2014), and a visiting Associate Professor at Kyoto University (July 2016-Nov 2016). He was invited to join UniProt Scientific Advisory Board in Sep 2018. His research focuses on developing and applying machine learning and data mining methods for Bioinformatics and Biomedical Informatics, especially biomedical text mining, protein function prediction, immunological informatics, drug discovery and Metagenomics.