Prof. Bijoy K. Ghosh
Texas Tech University, USA
Bijoy received the B. Tech and M. Tech degrees in Electrical and Electronics Engg. from BITS Pilani and the Indian Institute of Technology, Kanpur, India, and the Ph.D. degree in Engineering Sciences from the Decision and Control Group of the Division of Applied Sciences, Harvard University, Cambridge, MA, in 1977, 1979 and 1983, respectively. From 1983 to 2007 Bijoy was with the Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, USA, where he was a Professor and Director of the Center for BioCybernetics and Intelligent Systems. Currently he is the Dick and Martha Brooks Regents Professor of Mathematics and Statistics at Texas Tech University, Lubbock, TX, USA. He received the Donald P. Eckmann award in 1988 from the American Automatic Control Council, the Japan Society for the Promotion of Sciences Invitation Fellowship in 1997. He became a Fellow of the IEEE in 2000, and a Fellow of the International Federation on Automatic Control in 2014. Currently he is the IEEE Control Systems Society Representative to the IEEE-USA's Medical Technology Policy Committee. Bijoy had held visiting positions at Tokyo Institute of Technology, Osaka University and Tokyo Denki University, Japan, University of Padova in Italy, Royal Institute of Technology and Institut Mittag-Leffler, Stockholm, Sweden, Yale University, USA, Technical University of Munich, Germany, Chinese Academy of Sciences, China and Indian Institute of Technology, Kharagpur, India. Bijoy's current research interest is in BioMechanics and Control Problems in Rehabilitation.
Speech Title: "Iterative Learning Control Problems in Medical Rehabilitation"
Abstract: In this talk, I shall review learning control problems from the point of view of medical rehabilitation of stroke patients. After initially surveying the field, a new Cooperative Learning Control problem is introduced where a dynamical system is controlled by the sum of two controllers. Each of the two controllers, we design, has the structure of an iterative learning controller, which learns to track a desired, a priori chosen, output sequence. Once learned, the strength of one of the controller is reduced while this loss of control is iteratively transferred to the other controller. There is no direct communication between the two controllers and each controller updates iteratively, using the error signal between the system and the desired output. We show that ‘controller participation’ can be iteratively transferred until one controller has completely acquired full control of the closed loop system. An important application of the proposed cooperative control system is in rehabilitation of stroke patients, wherein a loss of control in the arm movement is initially aided by additive ‘functional electrical stimulus’ signals generated through a computer. Subsequently, with therapeutic recovery, dependence on the computer control is reduced while the patient learns to be self-reliant on his/her own motor control capability.
Prof. Yinglei Lai
The George Washington University, USA
Dr. Yinglei Lai is Professor of Statistics in the Department of Statistics at the George Washington University. His research interest is to develop statistical and computational methods in bioinformatics, computational biology and biostatistics. He received his B.S. in Information & Computation Sciences and Business Administration from the University of Science and Technology of China in 1999. Dr. Lai received his Ph.D. in Applied Mathematics (Computational Biology) from the University of Southern California in 2003. After his postdoctoral training at Yale University School of Medicine, he joined as a faculty member in the Department of Statistics at the George Washington University in 2004.
Speech Title: "On Poisson Models in the Analysis of RNA-seq Data"
Abstract: High-throughput genome-wide RNA sequencing (RNA-seq) data have been increasingly collected for biomedical studies. Differential expression analysis and correlation analysis of RNA-seq data are important to understand the biological functions of genes and how genes interact with each other. RNA-seq data are generally count-type observations. Furthermore, many genes have multiple isoforms. Therefore, it can be challenging to conduct differential expression and correlation analysis of RNA-seq data. Poisson and related models have been widely used in the analysis of RNA-seq data. We extend the Poisson model approach so that the wide range of RNA-seq observations can be accommodated. We also propose a multivariate approach for the correlation analysis of RNA-seq data. Our simulation study demonstrates the advantage of our method. We use the RNA-seq data collected by The Cancer Genome Atlas (TCGA) project to illustrate our method.
Prof. Edwin Wang
University of Calgary, Canada
Dr. Edwin Wang is Professor and AISH Chair in Cancer Genomics at the University of Calgary. He was a Senior Investigator at the National Research Council Canada and Professor at McGill University. He has a undergraduate training in Computer Science and a PhD training in Experimental Molecular Genetics (UBC, 2012). He is the member of the AACR-Cancer Systems Biology Think Tank, which consists of ~30 world leaders in the field for discussing key problems and cutting-edge directions. He is an Editor of PLoS Computational Biology, the top journal in the field of bioinformatics. He has edited the book of Cancer Systems Biology (2010), the first book of the field. His pioneering work of microRNA of singling networks opens the new research area: network biology of non-coding RNAs. His pioneering work of cancer network motifs has been featured in the college textbook, GENETICS (2014/2017) written by a Nobel Laureate, Dr. Hartwell and the father of systems biology, Dr. Hood.
Speech Title: "From Health Genomics to Intelligent Precision Health"
Abstract: Cancer is the leading cause of death and the
third largest burden in the healthcare system in the world. Each year, more
than 15 million new cancer patients are diagnosed and 7-8 million people die
from cancer in the world. Current precision oncology is focusing on cancer
treatment, however, with some notable exceptions, improvements in overall
survival and morbidity over the past few decades have been modest.
Historical data suggest that early detection of cancer is crucial for its
ultimate control and prevention. To meet the challenges of the surge in
cancer cases in the future, it is envisioned that, besides the promotion of
lifestyle changes, improving early diagnosis is the best strategy for
reducing the impact of carcinogenesis.
Both genetic and environmental factors (e.g., pollution, lifestyle and so on) interact to induce cancer initiation, progression and metastasis. Therefore, we are aiming to combine the genome sequencing, imaging and electronic medical records of individuals to identify high-risk cancer individuals, ‘healthy lifestyle patterns’ for cancer prevention, and monitor high-risk cancer individuals for cancer early detection. To do so, we have complied a cohort which contains 5 million people whose medical records have been collected. Among them, 0.5 million people’ genomic information has been determined. We are developing new algorithms by applying machine learning and deep learning approaches to the cohort to meet the goals mentioned above.
Assoc. Prof. Qijun Zhao
Sichuan University, China
Qijun Zhao is currently an associate professor in the College of Computer Science at Sichuan University. He obtained his B.Sc. and M.Sc. degrees in computer science both from Shanghai Jiao Tong University, and his Ph.D. degree in computer science from the Hong Kong Polytechnic University. He worked as a post-doc research fellow in the Pattern Recognition and Image Processing lab at Michigan State University from 2010 to 2012. His research interests lie in biometrics, particularly, face perception and affective computing, with applications to intelligent video surveillance, public security, healthcare, and human-computer interactions. Dr. Zhao has published more than 50 papers in academic journals and conferences, and participated in many research projects either as principal investigators or as primary researchers. He is a reviewer for many renowned field journals and conferences. He served as a program committee co-chair in organizing the 11th Chinese Conference on Biometric Recognition (CCBR2016) and the 2018 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA).
Speech Title: "3D Face Modeling: Images, Shapes, and DNA"
Abstract: The face reveals a lot of information of humans, for example, identity, race, gender, age, emotion, intention, and health. 3D face models are thus widely used in many applications, from security to healthcare, from education to entertainment, and from human-computer interaction to computer vision. Yet, acquisition of 3D faces is still much more expensive than acquisition of 2D face images. This talk will introduce our recent work on reconstructing 3D face shapes from 2D images, including 3D face reconstruction via cascaded regression in shape space, joint face alignment and 3D face reconstruction, disentangling features in 3D face shapes for joint face reconstruction and recognition, and mug-shot-based 3D face reconstruction for arbitrary view face recognition. To better understand the diversity of human 3D face shapes, this talk will analyze the impact of ethnicity on 3D face modeling, review related research on 3D face modeling from the biological perspective, and discuss future research directions. We believe that 3D faces will play increasingly important roles in many applications with the rapid development of both 3D face acquisition techniques and 3D face modeling methods.
Prof. Ralf Hofestädt
Bielefeld University, Germany
Prof. Ralf Hofestädt studied Computer Science and Bioinformatics at the University of Bonn. He finished his PhD 1990 (University Bonn) and his Habilitation (Applied Computer Science and Bioinformatics) 1995 at the University of Koblenz. From 1996 to 2001, he was Professor for Applied Computer Science at the University of Magdeburg. Since 2001, he is Professor for Bioinformatics and Medical Informatics at the University Bielefeld. The research topics of the department concentrate on biomedical data management, modeling and simulation of metabolic processes, parallel computing and multimedia implementation of virtual scenarios.
Speech Title: "Medical Omics for the Detection of Comorbidity between Asthma and Hypertension"
Abstract: In general, comorbidity between two diseases will point to a causal relationship, which may be explained by the presence of common pathways or biochemical processes. Furthermore, comorbidity may be the result of non-obvious cofounder effects, e.g. life-style or environmental factors, predisposing to multiple health problems. Hypertension is observed by around 30% of the adult population and its prevalence is growing together with the age. Hypertension causes many other cardiovascular diseases, including heart failure. Asthma is a chronic respiratory disease and seems to be the result of complex interactions between genetic and environmental risk factors. Many studies report association between asthma and hypertension in different patient cohorts showing that asthmatic patients are more predisposed to hypertension. Presence of hypertension, in turn, is associated with increased frequency and severity or asthma. Considering that both asthma and hypertension have a strong genetic component, several attempts to find shared genes in order to explain comorbidity between asthma and hypertension have been made. This talk will present the genetic analysis of both diseases and the side effects of the drug treatment in practise.
Dr. Mingxiang Teng
Harvard University, USA
Dr. Mingxiang received his B.Eng. (2006), M.Eng. (2008) and Ph.D. (2012) degrees in Computer Science from Harbin Institute of Technology. He is currently a Research Fellow in the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health. His research focuses on developing computational and statistical methods, tools and software to help understand genomics data, specifically, addressing the data challenges in genomics and epigenomics such as pre-processing, analysis and integration of high-throughput data.
Prof. Ming Chen
Zhejiang University, China
Ming Chen received his PhD in Bioinformatics from Bielefeld University, Germany, in 2004. Currently he is working as a full Professor in Bioinformatics at College of Life Sciences, Zhejiang University. His group research work mainly focuses on the systems biology, computational and functional analysis of non-coding RNAs, and bioinformatics research and application for life sciences. Prof. Chen is serving as an academic leader in Bioinformatics at Zhejiang University. He chairs the Bioinformatics society of Zhejiang Province, China. He is a committee member of Chinese societies for "Modeling and Simulation of Biological Systems", "Computational Systems Biology", "Functional Genomics & Systems Biology" and "Biomedical Information Technology".
Speech Title: "Single Cell Big Data Analysis"
Abstract: With the development of CyTOF and single cell sequencing technology, high dimension and large scale data have being accumulated, and the analysis of these data become indispensable. This talk will briefly introduce several bioinformatics approaches for analyzing such data. We developed a semi-automatic cell clustering platform to identify cell populations in flow cytometry data. We dissected global ccRCC metastasis associated lncRNAs based on single-cell RNA-seq data analysis. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a Mouse Cell Atlas (MCA).
Prof. Zhiwei Qiao
ShanXi University, China
Zhiwei Qiao received his PhD degree in transportation information engineering and control from Beijing Jiaotong University in 2011. He was a Postdoctoral Scholar and Visiting Professor with Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, USA, from August 2012 to August 2014 and from January 2017 to August 2017, respectively. He is currently a professor with School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China. His research interests include electron paramagnetic resonance imaging (EPRI), computed tomography (CT) and magnetic resonance imaging (MRI) etc. He mainly focuses on image reconstruction algorithm, signal processing and high performance computing. He has published a series of papers on CT and EPRI image reconstruction, especially two papers on Journal of Magnetic Resonance. Now, he is constructing the China-USA united lab for medical imaging, supported by Shanxi University and The University of Chicago.
Speech Title: "Study on EPR Oxygen Imaging for Oxygen-Image-Guided Precise Radiation"
Abstract: Electron paramagnetic resonance imaging (EPRI) can yield the 3-dimensional (3D) spatial distribution of the unpaired-electron spin-density (UESD) from which the spatial distribution of oxygen concentration within tumor tissue, referred to as the oxygen image, can be derived. In pulsed 3D EPRI, the 3D Radon transform is used for modeling the imaging process, and existing algorithms such as the standard 3D filtered-backprojection (FBP) can be used for reconstructing images through inverting the 3D Radon transform. However, the existing algorithms often require data collected at a large number of densely sampled projection views, which can lead to a prolonged data-acquisition time especially in in vivo animal EPR imaging. Therefore, there always exists a strong interest in shortening data-acquisition time through reducing the number of data samples collected in EPRI, and one approach is to acquire data at a reduced number of sparsely distributed projection views from which existing algorithms such as FBP may reconstruct images with sampling artifacts. In the work, we investigate and develop an optimization-based image reconstruction from data collected at sparse views in EPRI. Specifically, we design a convex optimization program to which the EPR image of interest is formulated as a solution and then tailor the primal-dual, Chambolle-Pock (CP) algorithm to reconstruct the image by solving the convex optimization program. We have performed studies using simulated and physical-phantom data on the verification and characterization of the optimization-based image reconstruction. Results of the studies suggest that the optimization-based image reconstruction may yield accurate reconstructions from sparse-view projections, thus enabling fast EPRI with reduced acquisition time.
Prof. Minghui Li
Soochow University, China
Dr. Minghui Li is currently a professor in School of Biology & Basic Medical Sciences at Soochow University. Dr. Li received her Ph.D. in Computational Biophysics from the State Key Laboratory of Theoretcial and Computational Chemistry at Jilin Univeristy, China in 2010. Upon completion of her Ph.D., Dr. Li spent two years as a Postdoctoral Fellow in Computational Biophysics at The State University of New York at Buffalo in the USA. Then she moved to National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH) and worked as a Postdoctoral Fellow and Research Fellow from 2012 to 2016. Dr. Li’s primary research interests are in understanding the mechanisms of molecular recognition in biological systems, identifying disease-causing/cancer driver nonsynonymous mutations and building the relationship between genotype and phenotype at the molecular and atomic level using computational biophysics-based and bioinformatics methods. She has balanced method development with the application of these powerful tools to relevant cancer related targets. She will continue her research towards developing and applying powerful computational methods and tools for understanding, identifying and predicting disease-causing nonsynonymous mutations as well as their molecular mechanism analysis of pathogenesis in collaboration with biologists.
Speech Title: "MutaBind Estimates and Interprets the Effects of Sequence Variants on Protein–Protein Interactions"
Abstract: Proteins engage in highly selective
interactions with their macromolecular partners. Sequence variants that
alter protein binding affinity may cause significant perturbations or
complete abolishment of function, potentially leading to diseases. There
exists a persistent need to develop a mechanistic understanding of impacts
of variants on proteins. To address this need we introduce a new
computational method MutaBind to evaluate the effects of sequence variants
and disease mutations on protein interactions and calculate the quantitative
changes in binding affinity. The MutaBind method uses molecular mechanics
force fields, statistical potentials and fast side-chain optimization
algorithms. The MutaBind server maps mutations on a structural protein
complex, calculates the associated changes in binding affinity, determines
the deleterious effect of a mutation, estimates the confidence of this
prediction and produces a mutant structural model for download. MutaBind can
be applied to a large number of problems, including determination of
potential driver mutations in cancer and other diseases, elucidation of the
effects of sequence variants on protein fitness in evolution and protein
design. MutaBind is available at
Previous Keynote Speakers
Prof. David Zhang
Hong Kong Polytechnic University, Hong Kong
Prof. Nozomu Hamada
Universiti Teknologi Malaysia (UTM), Malaysia
Prof. Wirachman Wisnoe
Universiti Teknologi MARA (UiTM), Malaysia