报告题目：Efficient genome-to-genome comparison for pattern discovery and sequence compression
I will present an efficient algorithm to detect maximal exact matches (MEMs) between pairwise genomes. The key idea of the algorithm is to conduct fixed sampling of k-mers in the query genome and Bloom filtering of index k-mers from the reference genome. I will also present an efficient clustering algorithm to group similar genomes for compression. The key idea is to use sketch distance to measure the similarities of genomes. Besides, I will also highlight some results from my other recent publications related to drug discovery and miRNA-lncRNA-mRNA interplay analysis.
Dr. Jinyan Li is a Professor of Data Science and Program Leader of Bioinformatics at the Advanced Analytics Institute, Faculty of Engineering & IT, University of Technology Sydney, Australia. He has been actively working on data mining and bioinformatics for 20 years. He has published 220 papers, including 120 papers in prestigious journals of data mining, machine learning, and computational biology. He is widely known for his pioneering research on the theories and algorithms of Emerging Patterns (EPs). One of these papers has received 1250 Google Scholar citations. Jinyan has a Bachelor degree of Science (Applied Mathematics) from National University of Defense Technology (China), a Master degree of Engineering (Computer Engineering) from Hebei University of Technology (China), and a PhD degree (Computer Science) from the University of Melbourne (Australia). More details of his research can be found at http://www.uts.edu.au/staff/jinyan.li