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Data Mining in Bioinformatics: Report on BIOKDD'03 Data Mining in Bioinformatics: Report on BIOKDD'03 Mohammed J. Zaki Jason T. L. Wang Hannu T. T. Toivonen Computer Science Computer Science Computer Science Department Department Department Rensselaer Polytechnic New Jersey Institute of University of Helsinki Institute Technology Helsinki, FIN-00014, Finland Troy, NY 12180, USA Newark, NJ 07102, USA [email protected].fi [email protected] [email protected] 1. FOREWORD The workshop proceedings contain 9 papers (out of 24 sub- Bioinformatics is the science of managing, mining, and in- missions) that were accepted for presentation at the work- terpreting information from biological sequences and struc- shop. Each paper was reviewed by three members of the tures. Genome sequencing projects have contributed to program committee. Along with 2 keynote talks by Steven an exponential growth in complete and partial sequence Salzberg (from TIGR) and Sorin Istrail (from Celera), we databases. The structural genomics initiative aims to cat- were able to assemble a very exciting program. A brief alog the structure-function information for proteins. Ad- overview of the invited talks and contributed papers appears vances in technology such as microarrays have launched the below. sub¯eld of genomics and proteomics to study the genes, pro- This workshop followed the previous two highly successful teins, and the regulatory gene expression circuitry inside the workshops: BIOKDD02 [2], held in Edmonton, Canada, and cell. What characterizes the state of the ¯eld is the flood of BIOKDD01 [1] held in San Francisco, CA. Online proceed- data that exists today or that is anticipated in the future; ings of the BIOKDD workshops are accessible at the follow- data that needs to be mined to help unlock the secrets of ing links: the cell. ² http://www.cs.rpi.edu/»zaki/BIOKDD01/ While tremendous progress has been made over the years, many of the fundamental problems in bioinformatics, such ² http://www.cs.rpi.edu/»zaki/BIOKDD02/ as protein structure prediction or gene ¯nding, are still open. Data mining will play a fundamental role in understanding ² http://www.cs.rpi.edu/»zaki/BIOKDD03/ gene expression, drug design and other emerging problems in genomics and proteomics. Furthermore, text mining will 2. KEYNOTE TALKS be fundamental in extracting knowledge from the growing The opening keynote talk for the morning session, entitled literature in bioinformatics. genome paleontology: discoveries from complete genomes, BIOKDD'03 was held in conjunction with the 9th ACM was delivered by Steven Salzberg, Senior Director of Bioin- SIGKDD International Conference on Knowledge Discovery formatics at The Institute for Genomic Research (TIGR). and Data Mining, in Washington, DC, in August 2003. The Steven de¯ned genome paleontology as the task of compar- goal of this workshop was to encourage KDD researchers to ing genomes to uncover: i) history of species, ii) genome take on the numerous challenges that Bioinformatics o®ers. transformations, iii) recent mutations such as Single Nu- The workshop featured keynote talks from noted experts in cleotide Polymorphisms (SNPs), and iv) evolution. He de- the ¯eld, and the latest data mining research in bioinformat- scribed the work his group has done on genome scale se- ics; it encouraged papers that proposed novel data mining quence alignments, the hunt for genome scale duplications, techniques for tasks such as: symmetric chromosomal inversions, Human genome analy- ² Gene expression analysis sis, horizontal gene transfers, and other interesting prob- lems. ² Protein/RNA structure prediction The opening keynote talk for the afternoon session, enti- tled the minimum informative subset problem, was given ² Phylogenetics by Sorin Istrail, Senior Director for Informatics Research at Celera Genomics/Applied Biosystems. Sorin introduced ² Sequence and structural motifs a recurring problem within bioinformatics, which he termed ² Genomics and Proteomics the minimum informative subset problem, de¯ned as follows: given a set of objects, ¯nd a minimum set of attributes that ² Gene ¯nding are informative. He highlighted several applications of this problem, for example, SNP selection and haplotype tagging, ² Drug design assay design, literature survey, and so on. ² Text mining in bioinformatics 3. CONTRIBUTED PAPERS 4. ACKNOWLEDGMENT The ¯rst session in the morning consisted for four papers. We would like to thank all the authors, invited speakers, and Zhenzhen Kou, William Cohen, and Robert F. Murphy attendees for contributing to the success of the workshop. (Carnegie Mellon University) presented their work on ex- Special thanks are due to the program committee for help tracting information from text and images for location pro- in reviewing the submissions. teomics. They introduced new text mining and OCR tech- niques for caption understanding, and ¯gure-text matching, 5. WORKSHOP CO-CHAIRS with applications to protein sub-cellular localization, from Mohammed J. Zaki, Rensselaer Polytechnic Institute, USA the text and images found in online journals. In the next Jason T.L. Wang, New Jersey Institute of Technology, USA paper Mark A. Krogel and Tobias Sche®er (Humboldt Uni- Hannu T.T. Toivonen, University of Helsinki, Finland versity, Germany) studied the e®ectiveness of information extraction, multi-relational, and multi-view learning for pre- dicting gene deletion experiments. To build a biologically relevant model, they utilized all available data such as un- 6. PROGRAM COMMITTEE labeled data, relational data and abstracts from research Srinivas Aluru, Iowa State University papers. Jinyan Li, Huiqing Liu and Limsoon Wong (Insti- Pierre Baldi, University of California, Irvine tute for Infocomm Research, Singapore) claimed that mean- Yi-Ping Phoebe Chen, Queensland University of Technol- entropy discretized features are e®ective for classifying high- ogy, Australia dimensional biomedical data. The paper studied empirical Mark Craven, University of Wisconsin feature selection heuristics, utilizing entropy, for classi¯ca- Hasan Jamil, Mississippi State University tion of biomedical datasets. They show results on reduction George Karypis, University of Minnesota in features obtained in practice. In the ¯nal paper of the Ross D. King, University of Wales, UK session, Marios Skounakis and Mark Craven (University of Stefan Kramer, Technical University of Munich, Germany Wisconsin, Madison) discussed their work on evidence com- Simon M. Lin, Duke University bination in biomedical natural-language processing. In many Zoran Obradovic, Temple University cases the individual entities or relations of interest are found Srini Parthasarathy, Ohio State University in multiple contexts in a corpus. They present a statistical Luc De Raedt, Albert-Ludwigs University, Germany approach to combine evidence across such multiple contexts. Tobias Sche®er,Otto-von-Guericke University, Germany They apply the work to protein names and protein-protein Mona Singh, Princeton University interactions from MEDLINE. Shin-Mu Vincent Tseng, National Cheng Kung University, The second session in the afternoon, had two papers. Alek- Taiwan sander Icev, Carolina Ruiz, and Elizabeth F. Ryder (Worces- Alfonso Valencia, National Center for Biotechnology, Spain ter Polytechnic Institute), proposed distance-enhanced as- Limsoon Wong, Institute for Infocomm Research, Singapore sociation rules for gene expression. They mine associa- Jiong Yang, University of Illinois, Urbana-Champaign tion rules among motifs extracted from promoter regions of genes. The rules are extended with distances between motifs, and show improvements in classi¯cation versus use 7. REFERENCES of traditional association rules. Kevin Y. Yip, David W. Cheung and Michael K. Ng (University of Hong Kong) pre- [1] M. J. Zaki, H. Toivonen, J. T. L. Wang. BIOKDD01: sented a highly-usable projected clustering algorithm for gene Workshop on Data Mining in Bioinformatics. In expression pro¯les. Most existing projected clustering algo- SIGKDD Explorations, Volume 3, Issue 2, pp 71-73, rithms depend on several user speci¯ed parameters, which January 2002. are often ill understood. Their new algorithm overcomes such limitations, and they study applications in mining gene [2] M. J. Zaki, H. Toivonen, J. T. L. Wang. BIOKDD02: expressions. Recent Advances in Data Mining in Bioinformatics. In In the ¯nal session, there were three papers. Li Zhang, SIGKDD Explorations, Volume 4, Issue 2, pp 112-114, Aidong Zhang and Murali Ramanathan (University of Buf- December 2002. falo), opened with enhanced visualization of time series through higher Fourier harmonics. They studied methods for visualizing gene expression datasets using higher Fourier harmonics projections. Hsiao-Mei Lu, Sumeet Gupta, and Yang Dai (University of Illinois, Chicago) gave methods for reducing large diagonals in kernel matrices through semidef- inite programming (SDP). Application of Support Vector Machines in Bioinformatics, may result in kernel matrices with large diagonal elements. The paper used SDP to re- duce large kernels. Finally, Xintao Wu, Yong Ye, Kalpathi, and R. Subramanian (University of North Carolina, Char- lotte), presented their work on interactive analysis of gene interactions using graphical gaussian model. The datasets came from DNA microarray studies; they propose an inter- active system to explore gene relations..
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