Prof. Yi Pan

Shenzhen University Advanced Technology, China

Dr. Yi Pan is Fellow of American Institute for Medical and Biological Engineering, Foreign Member of Russian Academy of Engineering, Foreign member of Ukrainian Academy of Engineering Science, Member of European Academy of Sciences and Arts, Fellow of the Royal Society for Public Health, Fellow of the Institute of Engineering and Technology, and Fellow of the Japan Society for the Promotion of Science.

Dr. Yi Pan is currently a Chair Professor and the Dean of College of Computer Science and Control Engineering at Shenzhen University of Advanced Technology, China and a Regents’ Professor Emeritus at Georgia State University, USA. He served as Chair of Computer Science Department at Georgia State University from 2005 to 2020. He has also served as an Interim Associate Dean and Chair of Biology Department during 2013-2017. Dr. Pan joined Georgia State University in 2000, was promoted to full professor in 2004, named a Distinguished University Professor in 2013 and designated a Regents' Professor (the highest recognition given to a faculty member by the University System of Georgia) in 2015. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. 

Title: Biological Multiple Sequence Alignment: Scoring Functions, Algorithms, and Evaluations

Abstract: Aligning multiple biological sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences' structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. MSA also has many applications in Next-Generation Sequencing (NGS) data analysis such aligning multiple short reads. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this talk, I will describe a new scoring method for use in biological multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our three new multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge and sequence consistency to produce biological meaningful sequence alignments. The sequence knowledge-based algorithm utilizes the existing biological sequence knowledge databases such as Swiss-Prot to guide sequence alignment. When sequence knowledge databases are not available, the sequence consistency-based algorithm can utilize the consistency information from the input sequence to achieve a similar effect. Experimental results and theoretical analysis indicate that our new scoring function and alignment algorithms truly improve the current best multiple sequence alignment algorithms.

Prof. Fangxiang Wu

University of Saskatchewan SK, Canada, IEEE Fellow, IET Fellow

Dr. FangXiang Wu is currently a full professor in the Departments of Computer Science, and the College of Engineering at the University of Saskatchewan. His research interests include Artificial Intelligence, Machine Learning, Computational Biology, Health Informatics, Medical Image Analytics, and Complex Network Analytics. His Google scholar citations are over 16300 and h-index is 67. He is among top 2% world’s scientists ranked by Stanford University since 2017. Dr Wu is serving as the editorial board member of several international journals (including IEEE TCBB, Neurocomputing, etc.) and as the guest editor of numerous international journals, and as the program committee chair or member of many international conferences. He is an IEEE Fellow and an IET Fellow. 

Title: Artificial Intelligence for ASD Diagnosis

Abstract: Autism Spectrum Disorder (ASD) is a common psychiatric disorder disease that typically causes impaired communication and compromised social interactions. Functional magnetic resonance imaging (fMRI) data is one of the common neuroimaging modalities for understanding human brain functionalities as well as the diagnosis and treatment of brain disorders. Artificial intelligence methods with functional magnetic resonance imaging (fMRI) are now providing the great opportunities for ASD diagnosis. In this talk, after brief introductions to ASD and machine learning, I present three machine learning models of our work in ASD image analysis, which include auto-encoder, semi-supervised autoencoder, and graph attention neural network based methods for ASD diagnosis.

Prof. Ming Chen

Zhejiang University, China

Prof. Ming Chen, director of the Bioinformatics Department at the College of Life Sciences, is a leading figure in the field of bioinformatics at Zhejiang University, China. In 2004, he obtained his Ph.D. in bioinformatics from Bielefeld University, Germany. He was seconded to the Fundamental Research Department of the Ministry of Science and Technology, served as president assistant, and was specially appointed dean at Inner Mongolia Minzu University.

Prof. Chen's research encompasses bioinformatics, systems biology, non-coding RNA transcriptomics, and precision medicine. He has published over 200 academic papers in peer-reviewed journals such as Cell, Nature, Nucleic Acids Research, and Bioinformatics, with a Google Scholar H-index exceeding 54. He has been included in the list of the top 2% of scientists globally.

Title: Big Data-Driven Biological Age Prediction and Aging Assessment

Abstract: Aging research has emerged as one of the most popular global research fields, empowered by big data and artificial intelligence. This talk centers on biological age prediction and the assessment of human aging and mortality risk. We propose a composite machine learning-based biological age (ML-BA) model, which is constructed based on biomarkers obtained from medical examination data. This model integrates multiple machine learning algorithms to yield a more accurate prediction of biological age. The composite ML-BA model shows a strong association with health risk indicators and various diseases, providing enhanced aging measurement capabilities and supporting the potential application of machine learning in aging research. Additionally, leveraging half a billion healthcare records, we have developed an AI model to assess human aging and mortality risk. The interpretable parameters not only serve as excellent reference indicators for mortality risk assessment but also offer guidelines for maintaining a healthy aging process. Combined with our developed HALD database, we can attempt to correlate lifestyles with molecular mechanisms, thereby providing a scientific basis for future precise interventions. Our approach employs state-of-the-art big data analysis techniques in bioinformatics for future aging research studies.

Prof. Shihua Zhang

University of Chinese Academy of Sciences, China, IEEE Member

Prof. Shihua Zhang, Ph.D. Chinese Biography Principal Investigator Associate Professor Academy of Mathematics and Systems Science, Chinese Academy of Science. His main research areas include operations research, statistics, and bioinformatics. Currently, his primary research interests focus on computational cancer genomics, epigenetics, systems biology, and network science. He has also visited and studied at research institutions such as the University of Southern California, the University of California, Los Angeles (UCLA), the National University of Singapore, and the University of Tokyo in Japan.

Title: Intelligent decoding of spatial biology

Abstract: Technological advances in spatial transcriptomics are critical for better understanding the structures and functions of tissues in biological research. The combination of intelligent or statistical algorithms and spatial transcriptomics has emerged to pave the way for deciphering tissue architecture. We have made great efforts to advance intelligent spatial transcriptomics and developed a group of STA- tools such as STAGATE, STAligner, STAMarker, STAGE, STASCAN and STALocaor. For example, we created a graph attention auto-encoder tool STAGATE to identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. We introduced STAligner for integrating and aligning ST datasets across different conditions, technologies, and developmental stages to enable spatially-aware data integration, simultaneous spatial domain identification, and downstream comparative analysis. We developed a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics. We developed STASCAN for deciphering fine-resolution cell-distribution maps in spatial transcriptomics.