Keynote Speaker
(in alphabet order)
Susan M. Bridges, Ph.D
Co-Director of the Institute for Digital Biology
Hearin Distinguished Professor
Mississippi State University
Title: Algorithms for Structural Annotation of Genomes
Abstract: Novel approaches for structural annotation of genomes are required if we are to extract the full value from the avalanche of genome sequence generated from new sequencing technologies. New approaches for annotating genomes using proteomics data and for defragmenting dispersed repeats on genomes are described.
Short Biography:
Susan Bridges co-founded the Institute for Digital Biology (IDB) at Mississippi State (MSU) with Dr. Shane Burgess and Dr. Dawn Luthe. She currently serves as Co-Director of the IDB and as a Distinguished Fellow of the MSU Life Science and Biotechnology Institute. The IDB maintains the AgBase database, an international resource for Gene Ontology annotation of agricultural species. Dr. Bridges holds a B.S. and M.S. in biology and an M.S. and Ph.D. in computer science. In addition to her position as Professor of Computer Science and Engineering at MSU, she also holds adjunct appointments in the Department of Biochemistry and Molecular Biology and in the Department of Basic Science in the College of Veterinary Medicine. She has authored over seventy-five scientific papers and funding for her research program totals over $16,000,000. Her current research is funded by the USDA, the National Science Foundation, the National Institutes of Health, the Department of Energy, and the U.S. Army Corps of Engineers. Her research focuses on data mining from hetereogenous genomic, trascriptomic and proteomic data and on new methods in proteomics.
JOYDEEP GHOSH
Schlumberger Centennial Chair Professor,
Dept. of Electrical & Computer Engineering, Univ of Texas, Austin
Director, IDEAL (Intelligent Data Exploration and Analysis Lab)
Member, GSC,
Dept. of Computer Sciences, UT and Dept. of Biomedical Engineering, UT
Homepage: http://www.ideal.ece.utexas.edu/~ghosh/
Title: Locating a Few Useful Clusters in Large Biological Datasets: A Tale of Two Viewpoints
Abstract: A key application of clustering data obtained from sources such as microarrays, protein mass spectroscopy and phylogenetic profiles, is the detection of functionally related genes. Typically, only a small number of functionally related genes form meaningful groups, while the rest need to be ignored. Additional complications arise when there are several irrelevant experimental conditions, when the useful clusters occur at different resolutions/scales, and when genes participate in multiple biological processes, leading to multiple cluster memberships.
Thus the corresponding data mining problem is to detect a small number of cohesive, possibly overlapping clusters in the data while ignoring irrelevant data portions. We will discuss two broad approaches to this problem: (a) a generative approach where one determines and fits a suitable probabilistic model to the data, and (b) a non-parametric approach inspired by Wishart’s remarkable but obscure mode analysis work from 1968. The pros and cons of the two approaches will be illustrated using results from gene expression data analysis.
Short Biography:
Joydeep Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. He joined the UT-Austin faculty in 1988 after being educated at IIT Kanpur, (B. Tech '83) and The University of Southern California (Ph.D’88). He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab) and a Fellow of the IEEE. His research interests lie primarily in intelligent data analysis, data mining and web mining, adaptive multi-learner systems, and their applications to a wide variety of complex engineering and AI problems.
Dr. Ghosh has published more than 250 refereed papers and 35 book chapters, and co-edited 20 books. His research has been supported by the NSF, Yahoo!, Google, ONR, ARO, AFOSR, Intel, IBM, Motorola, TRW, Schlumberger and Dell, among others. He received the 2005 Best Research Paper Award from UT Co-op Society and the 1992 Darlington Award given by the IEEE Circuits and Systems Society for the Best Paper in the areas of CAS/CAD, besides ten other "best paper" awards over the years. He was the Conference Co-Chair of Computational Intelligence and Data Mining (CIDM’07), Program Co-Chair for ICPR'08 (Pattern Recognition Track), The SIAM Int'l Conf. on Data Mining (SDM'06), and Conf. Co-Chair for Artificial Neural Networks in Engineering (ANNIE)'93 to '96 and '99 to '03. He is the founding chair of the Data Mining Tech. Committee of the IEEE CI Society. He also serves on the program committee of several top conferences on data mining, neural networks, pattern recognition, and web analytics every year. Dr. Ghosh has been a plenary/keynote speaker on several occasions such as ISIT'08, ANNIE’06, MCS 2002 and ANNIE'97 and, and has widely lectured on intelligent analysis of large-scale data. He has co-organized workshops on high dimensional clustering (ICDM 2003; SDM 2005), Web Analytics (with SIAM Int'l Conf. on Data Mining, SDM2002), Web Mining (with SDM 2001), and on Parallel and Distributed Knowledge Discovery (with KDD-2000).
Dr. Ghosh has served as a co-founder, consultant or advisor to successful startups (Stadia Marketing, Neonyoyo and Knowledge Discovery One) and as a consultant to large corporations such as IBM, Motorola and Vinson & Elkins. At UT, Dr. Ghosh teaches graduate courses on data mining, artificial neural networks, and web analytics. He was voted the Best Professor by the Software Engineering Executive Education Class of 2004.
Xiaohua Tony Hu, Ph.D.
College of Information Science and Technology, Drexel University, Philadelphia, PA, 19104
Office: Rush Building 133, Phone: 215-895-0551, Email: thu AT cis.drexel.edu
Homepage: http://www.cis.drexel.edu/faculty/thu/
Title: Mining, Modeling and Evaluation of the Biomolecular Network
Abstract: Biomolecular networks dynamically respond to stimuli and implement cellular function, and understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions, and in general this presents the need for computer simulation to manipulate and understand the biomolecular network model. In this talk, we present a novel method to mine, model and evaluate a regulatory system executing cellular functions which can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to such biomolecular network to obtain various sub-networks. Second, computational models are generated for the sub-networks and simulated to predict their behavior in the cellular context. We discuss and evaluate some of the advanced computational modeling approaches; in particular, state-space modeling, probabilistic Boolean network modeling, and fuzzy logic modeling. The modeling and simulation results represent hypotheses that are tested against high-throughput biological datasets (microarrays and/or genetic screens) under normal and perturbation conditions. Experimental results on time-series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large biomolecular networks.
Short Biography:
Xiaohua (Tony) Hu is currently an associate professor (early tenured in 2007) and the founding director of the data mining and bioinformatics lab at the College of Information Science and Technology, one of the best information science schools in USA (ranked as #1 in 1999 and #5 in 2007-2008 in information systems by U.S. News & World Report). He is the now also serving as the IEEE Computer Society Bioinformatics and Biomedicine Steering Committee Chair, and the IEEE Computational Intelligence Society Granular Computing Technical Committee Chair (2007-2008). Tony is a scientist, teacher and entrepreneur. He joined Drexel University in 2002, founded the International Journal of Data Mining and Bioinformatics (SCI indexed) in 2006, International Journal of Granular Computing, Rough Sets and Intelligent Systems in 2008. Earlier, he worked as a research scientist in the world-leading R&D centers such as Nortel Research Center, GTE labs and HP Labs. In 2001, he founded the DMW Software in Silicon Valley, California. His research ideas have been integrated into many commercial products and applications.
Tony’s current research interests are in biomedical literature data mining, bioinformatics, text mining, semantic web mining and reasoning, rough set theory and application, information extraction and information retrieval. He has published more than 160 peer-reviewed research papers in various journals, conferences and books such as various IEEE/ACM Transactions (IEEE/ACM TCBB, IEEE TFS, IEEE TDKE, IEEE TITB, IEEE Computer), JIS, KAIS, CI, DKE, IJBRA, SIG KDD, IEEE ICDM, IEEE ICDE, SIGIR, ACM CIKM, IEEE BIBE, IEEE CICBC etc, co-edited 14 books/proceedings. He has received a few prestigious awards including the 2005 National Science Foundation (NSF) Career award, the best paper award at the 2007 International Conference on Artificial Intelligence, the best paper award at the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, the 2007 IEEE Bioinformatics and Bioengineering Outstanding Contribution Award, the 2006 IEEE Granular Computing Outstanding Service Award, and the 2001 IEEE Data Mining Outstanding Service Award. He has also served as a program co-chair/conference co-chair of 14 international conferences/workshops and a program committee member in more than 50 international conferences in the above areas. He is the founding editor-in-chief of the International Journal of Data Mining and Bioinformatics (SCI indexed), International Journal of Granular Computing, Rough Sets and Intelligent Systems, an associate editor/editorial board member of four international journals (KAIS, IJDWM, IJSOI and JCIB). His research projects are funded by the National Science Foundation (NSF), US Dept. of Education, and the PA Dept. of Health and he has obtained more than US$3.8 millions research grants in the past 4 years as PI or Co-PI.
Sun Kim
Chair, Director of Bioinformatics Program in Indiana University (IU) School of Informatics
Director of Center for Bioinformatics Research
Associate Professor in the School of Informatics
Title: A Data-driven, Systems Biology Approach for Exploring the Role of Epigenetics in Drug-resistant Human Cancer
Abstract: This talk will summarize a project to investigate the role of epigenetics in the development of the drug resistant phenotype in human cancer, a major research emphasis of the of the Ohio State University-Indiana University Integrated Cancer Biology Center.
Short Biography:
Sun Kim is a Chair, Director of Bioinformatics Program in Indiana University (IU) School of Informatics, Director of Center for Bioinformatics Research, an Associate Professor in the School of Informatics, an Associated Faculty at Center for Genomics and Bioinformatics, an Adjunct Associate Professor of Celluar and Integrative Physiology, Medical Sciences Program, an Affiliated Faculty at the Biocomplexity institute at IU Bloomington. Prior to IU, he worked at DuPont Central Research and at the University of Illinois at Urbana-Champaign. Sun Kim received B.S and M.S and Ph.D in Computer Science from Seoul National University, Korea Advanced Institute of Science and Technology (KAIST), and the University of Iowa respectively. Sun Kim is a recipient of Outstanding Junior Faculty Award at Indiana University 2004, USA NSF CAREER Award from 2003 to 2008, and Achievement Award at DuPont Central Research in 2000.
His research areas are machine learning and string pattern matching algorithms with primary focus on their application to microbial genome analysis and cancer epigenomics. His lab is also pioneering to develop a new bioinformatics information system development paradigm using cloud computing and graphical workflow composers. He is actively contributing to the bioinformatics community, serving on the editorial board for journals including co-editor-in-chief for International Journal of Data Mining and Bioinformatics, and co-organizing many scientific meetings including IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2008 as a co-program chair and 2009 as a co-conference chair.
Dr. Yi Pan
Chair and Professor of Computer Science
Professor of Computer Information Systems
Department of Computer Science Georgia State University
Homepage: http://www.cs.gsu.edu/pan/
Title: Protein Structure Prediction and its Understanding Based on Machine Learning Methods
Abstract: Understanding protein structures is vital to determining the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein structures from protein sequences using computer programs is an important step to unveil proteins' three dimensional conformation and functions. As a result, prediction of protein structures has profound theoretical and practical influence over biological study. The explanation of how a decision is made during prediction is also important for improving protein structure prediction and guiding the "wet experiments". In this talk, we will show how to use machine learning methods to improve the accuracy of protein structure prediction and to interpret prediction results. We will report our research on using neural networks, Support Vector Machines combined with Decision Tree and Association Rule for protein structure prediction, rule extraction and prediction interpretation. Evaluation and comparisons of various prediction and rule extraction systems will be presented and future research direction in this area will also be identified.
Short Biography:
Dr. Pan's research interests include parallel and distributed computing, optical networks, wireless networks, and bioinformatics. Dr. Pan has published more than 100 journal papers with 30 papers published in various IEEE journals. In addition, he has published over 100 papers in refereed conferences (including IPDPS, ICPP, ICDCS, INFOCOM, and GLOBECOM). He has also co-authored/co-edited 33 books (including proceedings) and contributed several book chapters. His pioneer work on computing using reconfigurable optical buses has inspired extensive subsequent work by many researchers, and his research results have been cited by more than 100 researchers worldwide in books, theses, journal and conference papers. He is a co-inventor of three U.S. patents (pending) and 5 provisional patents, and has received many awards from agencies such as NSF, AFOSR, JSPS, IISF and Mellon Foundation. His recent research has been supported by NSF, NIH, NSFC, AFOSR, AFRL, JSPS, IISF and the states of Georgia and Ohio. He has served as a reviewer/panelist for many research foundations/agencies such as the U.S. National Science Foundation, the Natural Sciences and Engineering Research Council of Canada, the US-Israel Binational Science Foundation, the Australian Research Council, the Swedish Research Council, and the Hong Kong Research Grants Council. Dr. Pan has served as an editor-in-chief or editorial board member for 15 journals including 5 IEEE Transactions and a guest editor for 10 special issues for 9 journals including 2 IEEE Transactions. He has organized several international conferences and workshops and has also served as a program committee member for several major international conferences such as INFOCOM, GLOBECOM, ICC, IPDPS, and ICPP.
Professor Patrick Shen-Pei Wang
Dr. Patrick S.P. Wang, Professor & IAPR Fellow & IEEE Outstanding Achievment Awardee
College of Computer and Information Science of Northeastern University, Boston, MA, USA
Homepage: http://www.ccs.neu.edu/home/pwang/
Title: Intelligent Pattern Recognition and Applications to Similarity Measurement
Abstract: Time is money. Saving time is saving money. Every penny saved is one penny earned. This research discusses two major concerns that we can consider saving time: communication by post mails, and, transportation on roads, by intelligent pattern recognition, in terms of hierarchical structures and ambiguities.
Short Biography:
He is Fellow of IAPR (International Association of Pattern Recognition) and co-founding chief editor of IJPRAI(International Journal of Pattern Recognition and Artificial Intelligence)
(with Germany's (Deutschland) Prof. Xiaoyi Jiang ), and WSP MPAI Book Series (with Switerland's Prof. Horst Bunke ).
In Fall 1996, Dr. Wang has been elected as Otto-Von-Guericke Distinguished Guest Professor at the Graphics and Simulations Lab of the University of Magdeburg (near Berlin) of Deutschland (Germany).
Dr. Wang has been visiting at University of Calgary's Biometrics Technology Laboratory , Calgary, Canada from 2006-2007 as iCORE (Informatics Circle of Research Excellence) visiting Professor , where he publised his 23rd book in July 2007, entitled : IMAGE PATTERN RECOGNITION --- Synthesis and Analysis in Biometrics by WSP (World Scientific Publishing Co.) headquartered at Singapore. He is now Zi-Jiang Visiting Chair Professor at the Department of Computer Science and Technology of prestigious East China Normal University, Shanghai, China. Professor Wang has also served as Honorary Advisor Professor of Xiamen University and Sichuan University of China .
Dong Xu, Ph.D.
James C. Dowell Distinguished Professor
Director, Digital Biology Laboratory
Chair, Computer Science Department
University of Missouri
Title: New Frontiers in Translational Research with Next-Generation Sequencing and Proteomics Analyses
Time: August 4th, 2009
Abstract: Recent advances in next-generation sequencing and proteomics further empowered translational research in medicine and agriculture. Based on new technologies, systematic approaches for personalized medicine, bioenergy, and transgenic crops are being developed. These developments will have major social and economical impact. New technologies also pushed frontiers towards large-scale studies of more complex systems, such as non-coding RNA, post-translational modifications, and DNA methylations. These studies are typically data rich and require extensive bioinformatics developments. I will give a number of examples showing the roles of computer science and bioinformatics in the related developments.
Short Biography:
Dong received his Ph.D. in computational biophysics from the Beckman
Institute at the University of Illinois at Urbana-Champaign. He was a
research scientist at Oak Ridge National Labortary, United States
Department of Energy and National Institutes of Health, United States
Department of Health and Human Services, before he was named chair of
computer science and engineering at the University of Missouri.
Professor Dong Xu is a program chair of IEEE Bioinformatics and
Bioengineering 2009 International Conference in Washington DC and an
elected fellow of International Society of Intelligent Biological
Medicine. He is currently James C. Dowell Distinguished Professor of
Computer Science and Engineering, Director of Digital Biology
Laboratory, and Chair of Computer Science Department at the University
of Missouri main campus in Columbia.
Aidong Zhang
Department of Computer Science and Engineering
University at Buffalo, The State University of New York
U.S.A.
Homepage: http://www.cse.buffalo.edu/faculty/azhang
Title: Computational Analysis of Biological Networks
Abstract: The development of computational techniques for the effective analysis of biological datasets is a crucial step in the medical application of bioinformatics. This unique merging of computer science and biomedical expertise is expected to provide the synergy needed to advance biomedical research to the next level. When analyzing biological data we face many new computational challenges. Algorithms that are specifically designed for biological data are required so that we can take advantage of their unique features and address the unique problems they raise. In this talk, I will discuss new computational research issues and approaches to analysis of biological networks, especially protein interaction networks.
Short Biography:
Dr. Aidong Zhang is a professor and Chair of the Department of Computer Science and Engineering at the State University of New York at Buffalo and the director of the Buffalo Center for Biomedical Computing (BCBC). She is an author of more than 200 research publications and has served on many editorial boards of prestigious journals. Dr. Zhang is a recipient of the National Science Foundation CAREER Award and SUNY (State University of New York) Chancellor’s Research Recognition Award. Dr. Zhang is an IEEE Fellow.
Prof. Zhi-Hua Zhou
Cheung Kong Professor
Department of Computer Science & Technology, Nanjing University, China
Homepage: http://cs.nju.edu.cn/zhouzh/
Title: MIML: A New Machine Learning Framework with Application to Drosophila Gene Expression Pattern Annotation
Abstract: A new machine learning framework, MIML (Multi-Instance Multi-Label learning), was proposed recently. In this framework, an object is represented by multiple feature vectors, and is allowed to be associated with multiple class labels simultaneously. This framework is particularly helpful for learning with complicated objects, and has been found well useful in applications such as image categorization and text categorization.
The Berkeley Drosophila Genome Project (BDGP) has generated a large amount of gene expression patterns during Drosophila embryogenesis. Automating the annotation process is very desired. However, there is a big challenge; that is, the anatomical and developmental ontology terms are body-part related and present in local regions of images, while in the BDGP, they are attached collectively to groups of images and it is unknown which term is assigned to which region of which image in the group. MIML provides a promising way to address the challenge.
This talk will give an introduction to MIML as well as the application to Drosophila Gene Expression Pattern Annotation.
Short Biography:
Zhi-Hua Zhou is currently Cheung Kong Professor and Head of the LAMDA group affiliated with both the Department of Computer Science & Technology and the National Key Laboratory for Novel Software Technology at Nanjing University, China. He has wide research interests, mainly including artificial intelligence, machine learning, data mining, information retrieval and pattern recognition. In these areas he has published over 60 papers in leading journals and conferences. He has won various awards or honors. He is an associate editor-in-chief of <Chinese Science Bulletin>, associate editor of <IEEE Transactions on Knowledge and Data Engineering>, and on the editorial boards of <Artificial Intelligence in Medicine> (Elsevier), <Intelligent Data Analysis> (IOS), <Knowledge and Information Systems> (Springer), <Journal of Computer Science & Technology> (Springer), <Science in China>, etc. He is/was PAKDD Steering Committee member, program committee chair/co-chair of PAKDD'07 and PRICAI'08, vice chair or area chair of conferences including IEEE ICDM'06, IEEE ICDM'08, Australasian AI'08, SIAM DM'09, etc., and chaired a dozen of native conferences. He is the chair of the CAAI Machine Learning Society, vice chair of the CCF Artificial Intelligence & Pattern Recognition Society and chair of the IEEE Computer Society Nanjing Chapter.
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