2023 5th International Conference on Advanced Bioinformatics and Biomedical Engineering (ICABB 2023) aims to gather professors, researchers, scholars and industrial pioneers all over the world. ICABB is the premier forum for the presentation and exchange of past experiences and new advances and research results in the field of theoretical and industrial experience. The conference welcomes contributions which promote the exchange of ideas and rational discourse between educators and researchers all over the world. We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. ICABB 2023 is welcome qualified persons to delivery a speech in the related fields. If you are interested, please send a brief CV with photo to the conference email box: email@example.com.
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Prof. Sung Wing Kin
National University of Singapore, Singapore
Prof. Dr. Wing-Kin Sung received both the B.Sc. and the Ph.D. degree in the Department of Computer Science from the University of Hong Kong in 1993, 1998, respectively. He is a professor in the Department of Computer Science, School of Computing, NUS. Also, he is a senior group leader in Genome Institute of Singapore. He has over 20 years experience in Algorithm and Bioinformatics research. He also teaches courses on bioinformatics for both undergraduate and postgraduate. He was conferred the 2003 FIT paper award (Japan), the 2006 National Science Award (Singapore), and the 2008 Young Researcher Award (NUS) for his research contribution in algorithm and bioinformatics.
Speech Title: "Revisit the Problem of Virus Integration Calling Using Next Generation Sequencing"
Abstract: A significant portion of human cancers are due to viruses integrating into human genomes. Therefore, accurately predicting virus integrations can help uncover the mechanisms that lead to many devastating diseases. Virus integrations can be called by analysing the high-throughput sequencing datasets. This talk discusses the effort on virus integration calling in the last 10 years. Then, we describes our latest work. We found that existing methods fail to report a significant portion of virus integrations, while predicting a large number of false positives. We observe that the inaccuracy is caused by incorrect alignment of reads in repetitive regions. False alignments create false positives, while missing alignments create false negatives. We proposes a novel method SurVirus, an improved virus integration caller, that corrects the alignment of reads which are crucial for the discovery of integrations. We show that SurVirus is significantly more precise than existing methods while it also detects many novel integrations previously missed by other tools, most of which are in repetitive regions. We validate a subset of these novel integrations, and find that the majority are correct. Using SurVirus, we find that HPV and HBV integrations are enriched in LINE and Satellite regions which had been overlooked, as well as discover recurrent HBV and HPV breakpoints in human genome-virus fusion transcripts.
Assoc. Prof. Jia Meng
Xi’an Jiaotong-Liverpool University, China
Jia Meng received his bachelor degree in Electrical Engineering from Northwestern Polytechnic University in 2006. He earned his PhD in Electrical Engineering from University of Texas at San Antonio in 2011. He joined Massachusetts Institute of Technology in Feb 2012 as a Bioinformatician and the Supervisor of Bioinformatics Core Facility at Picower Institute for Learning and Memory. Between 2012 and 2014, he served as an Associate Scientist at Broad Institute of MIT and Harvard. He is now a Senior Associate Professor at Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, and has an honorary appointment from Institute of Integrative Biology, University of Liverpool. Jia Meng has previously worked on a wide variety of computational biology projects that aim at a system level understanding of gene regulation and the integration of multiple high-throughput data types and databases with advanced multivariate techniques, such as, Bayesian generative modelling, sparse representation, factorization, nonparametric approaches, etc. He has authored or co-authored more than 70 peer-reviewed publications since 2008 and is now focusing primarily on epitranscriptome bioinformatics.
Speech Title: "Deciphering the Distribution of mRNA-related Features in the Presence of Isoform Ambiguity"
Abstract: The distribution of biological features strongly indicates their functional relevance. Compared to DNA-related features, deciphering the distribution of mRNA-related features is non-trivial due to the existence of isoform ambiguity and compositional diversity of mRNAs. We propose here a rigorous statistical framework, MetaTX, for deciphering the distribution of mRNA-related features. Through a standardized mRNA model, MetaTX firstly unifies various mRNA transcripts of diverse compositions, and then corrects the isoform ambiguity by incorporating the overall distribution pattern of the features through an EM algorithm. MetaTX was tested on both simulated and real data. Results suggested that MetaTX substantially outperformed existing direct methods on simulated datasets, and that a more informative distribution pattern was produced for all the three datasets tested, which contain N 6-Methyladenosine sites generated by different technologies. MetaTX should make a useful tool for studying the distribution and functions of mRNA-related biological features, especially for mRNA modifications such as N 6-Methyladenosine.