The Pennsylvania State University
Nanyang Technological University
(PRIB 2009 General Chair)
The University of Sheffield
University of Windsor
National Institute of Biomedical Innovation
Dana-Farber Cancer Institute
Natural Selection Inc.
University of Melbourne
La Trobe University
Auckland University of Technology
Russian Academy of Sciences
University of Arkansas at Little Rock
Indian Statistical Institute
Kent State University
Massachussetts Institute of Technology
Simon Fraser University
National University of Singapore
Wellcome Trust Sanger Institute
Georgia State University
The goal of the IAPR TC20 is to bring together pattern recognition scientists and life scientists to find solutions to problems in bioinformatics, and foster multidisciplinary research in the pattern recognition community.
In order to achieve its goal, the IAPR TC-20 will expand its activities in the following areas:
1. Education: through the website, educational materials such as lecture notes, tutorials, etc., will be made available.
2. Research: a database of bioinformatics applications, literature, tools, and benchmark datasets will be maintained.
3. Events: organize PRIB conferences, special sessions at conferences, special issues of journals, and competitions, etc.
In the post-genomic era, a holistic understanding of biological systems and processes, in all their complexity, is critical in comprehending nature’s choreography of life. As a result, bioinformatics involving its two main disciplines, namely the life sciences and the computational sciences, is fast becoming a very promising multidisciplinary research field. With the ever increasing application of large-scale high-throughput technologies, such as gene or protein microarrays and mass spectrometry methods, the enormous body of information is growing rapidly. Bioinformaticians are posed with a large number of difficult problems to solve, arising not only due to the complexities in acquiring the molecular information but also due to the size and nature of the generated data sets and/or the limitations of the algorithms required for analyzing this data. Although the field of bioinformatics is still in its embryonic stage, the recent advancements in computational and information-theoretic techniques are enabling us to conduct various in silico testing and screening of many lab-based experiments before these are actually performed in vitro or in vivo. These in silico investigations are providing new insights for interpretation and establishing new direction for a deeper understanding. Amongst the various advanced computational methods currently being applied to such studies, the pattern recognition techniques are mostly found to be at the core of the whole discovery process for apprehending the underlying biological knowledge. Thus, we can safely surmise that the ongoing bioinformatics revolution may, in future, inevitably play a major role in many aspects of medical practice, and/or the discipline of life sciences.
Bioinformatics is aimed at discovering knowledge from life sciences data with the aid of Information Technology, to find answers to unresolved problems in biology. One of the important discoveries of pattern recognition in bioinformatics is that specific patterns of our genomes and proteomes are able to tell our characters and how prone we are for certain diseases. In the coming years, medical practitioners will be able to personalize our medication by just looking at these patterns.
· Computational and comparative genomics.
· Functional genomics
· Structural bioinformatics and proteomics
· Cheminformatics, chemigenomics
· Systems biology, pathway analysis
· Phylogenic analysis of species, sequences, structures, etc
· Drug discovery, pharmacogenomics
· Medical informatics
· Molecular and cellular imaging
Pattern Recognition for Bioinformatics
The past decade has witnessed an explosion and implosion of the amount and complexity of bioinformatics data such as DNA and protein sequences, gene and protein expressions, structures, pathways, genetic information, biomedical text data, and molecular images. Although the analyses of these data involve pattern recognition and data mining, novel and efficient data analysis techniques are yet to be discovered to realize their true potential.
DNA molecules store the blueprint of cell function. Information stored in DNA, a chain of four nucleotides (A, T, G, and C), is first transcribed to mRNA and then translated to the functional form of life, proteins. The initiation of translation or transcription process depends on the presence of specific signals and patterns, referred to as motifs, present in DNA and RNA. Research on in silico detection of specific patterns of DNA sequences such as genes, binding sites, and promoters, leads to better understanding of molecular level function of a cell. Comparative genomics focus on comparison of different genomes to find conserved patterns or significant mutations over the evolution, which could possess some functional significance. Construction of evolutionary trees is useful to infer how genome and proteome are evolved and branch across species by ways of a complete library of motifs and genes.
A protein’s functionality or interaction with other proteins is mainly determined by its 3-D structure. Prediction of protein’s 3-D structure from its 1-D amino-acid sequence remains an important problem in structural genomics; protein-protein interactions are responsible for most molecular functions in living cells. Computational modeling and visualization tools of 3-D structures of proteins and interaction help biologists to infer cellular activities.
The challenge in functional genomics is to analyze gene expressions accumulated by microarray techniques to discover co-regulated genes and thereby gene regulatory networks. Discovering and understanding how genes and proteins interact in specific pathways are gateways to systems biology. Molecular and cellular imaging provides techniques for in vivo sensing or imaging of cellular events such as movement of cells and sub-cellular localization of proteins. Potential techniques to fuse and integrate different types of life sciences data are yet to be realized.
The ever expanding knowledge of biomedical and phenotype data, combined with genotypes, is becoming difficult to be analyzed by traditional methods. Advanced data mining techniques, where the use of metadata for constructing precise descriptors of medical concepts and procedures, are required in the field of medical informatics. The vast amount of biological literature is posing new challenges in the field of text mining. These text mining techniques along with the aid of information fusion methods could help find pathways and interaction networks.
Today, high throughput and high content screening techniques allow biologists to gather data at an unprecedented rate. However, pattern recognition techniques to make inferences from these data are not evolving at a rate sufficient to meet the demand.
The International Conference on Pattern Recognition in Bioinformatics (PRIB) is the major event of IAPR TC-20, organized. The aim of this conference is to provide an opportunity to academia, researchers, scientists and industry professionals to present their latest research in pattern recognition and computational intelligence based techniques applied to problems in bioinformatics and computational biology. It also provides them with an excellent forum to interact with each other and share experiences.
1. Fifth IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 20010), Nijmegen, The Netherlands, September 22 - 24, 2010.
2. Fourth IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2009), Sheffield, United Kingdom, September 7 - 9, 2009.
3. Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008), Melbourne, Australia, October 15 – 17, 2008.
4. Second IAPR International Workshop on Pattern Recognition in Bioinformatics (PRIB 2007), Singapore, October 1 – 2, 2007.
5. First International Workshop on Pattern Recognition in Bioinformatics (PRIB 2006), Hong Kong, China, August 20, 2006.
· Machine Learning for Integrative Genomics (J. Hollmen, M. Dugas and S. Bicciato)
WCCI – IJCNN 2008
· Analysis of gene and protein expression data (V. Kuznetsov and J. Rajapakse)
· Prediction of protein structures and features (S. Ahmad and M. Gromiha)
1. Special Issue on “Pattern Discovery from Bioinformatics Data”, IEEE Engineering in Medicine and Biology Magazine. To appear in 2009.
2. Special Issue on “Pattern Recognition in Bioinformatics”, Pattern Recognition Letters. To appear in 2010.
3. Special Issue on “Computational Intelligence in Bioinformatics”, Neurocomputing Journal. To appear in 2010.
4. V. Kadirkamanathan, G. Sanginetti, M. Girolami, M. Niranjan, and J. Noirel, (Eds.), Pattern Recognition in Bioinformatics: Second IAPR International Workshop, PRIB 2009, Sheffield, UK, Lecture Notes in Computer Science (Sub-series: Lecture Notes in Computer Science), Vol. 5780, Sep 2009, ISBN 978-3-642-04030-6.
5. M. Chetty, A. Ngom, and S. Ahmad, (Eds.), Pattern Recognition in Bioinformatics: Second IAPR International Workshop, PRIB 2008, Melbourne, Australia, Lecture Notes in Computer Science (Sub-series: Lecture Notes in Computer Science), Vol. 5265, Oct 2008, ISBN 978-3-540-88434-7.
6. IAPR Newsletter article, Volume 30, Number 1, January 2008
7. J. C. Rajapakse, B. Schmidt, and G. Volkert (eds.) Pattern Recognition in Bioinformatics: Second IAPR International Workshop, PRIB 2007, Singapore, Lecture Notes in Computer Science (Sub-series: Lecture Notes in Bioinformatics), Vol. 4774, Oct 2007, ISBN 978-3-540-75285-1
8. J. C. Rajapakse, L. Wong, and R. Acharya (eds.) Pattern Recognition in Bioinformatics: International Workshop, PRIB 2006, Hong Kong, Lecture Notes in Computer Science (Sub-series: Lecture Notes in Bioinformatics), Vol. 4146, August 2006, ISBN 3-540-37446-9, 183 pages
The IAPR TC-20 includes the PRIB Technical Committee, membership of which is open to all those who have interest in bioinformatics research. The membership to the TC-20 Committee is by invitation from the TC-20 Chair. The TC-20 Committee Membership has a world-wide representation and makes decisions on the TC-20 activities. Enquiries on membership for IAPR TC-20 Committee or PRIB Technical Committee should be directed via email to the TC-20 Chair: Dr. Madhu Chetty (madhu ‘dot’ chetty ‘at’ infotech ‘dot’ monash ‘dot’ edu ‘dot’ au). For web queries, contact the TC-20 Web Chair: Dr. Alioune Ngom (angom ‘at’ cs ‘dot’ uwindsor ‘dot’ ca). For information queries, contact the TC-20 Information Chair, Dr. Shandar Ahmad (shandar ‘at’ netasa ‘dot’ org).
Selected Publications of IAPR TC-20 Members
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Benchmark Data Sets
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Last edited on June 27, 2009