International Association for Pattern Recognition

Technical Committee 20 on
Pattern Recognition for Bioinformatics


 

Prof. Raj Acharya, Chair, IAPR-TC20

acharya@cse.psu.edu

www.cse.psu.edu/~acharya

 

 

Assoc. Prof. Jagath C. Rajapakse, Vice Chair, IAPR-TC20

asjagath@ntu.edu.sg

www.ntu.edu.sg/home/asjagath

  

Goal:

The goal of 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. One of the goals is to facilitate and bring together the pattern recognition and the life science communities.

The committee has a membership list and a website for dissemination and exchange of ideas among members. Its website will have links to other websites of similar interests, the information on conferences and likely events, and useful data sources.

 

Interest Areas:

The past decade has witnessed an explosion in the amount and complexity of biological 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, the novel and efficient data analyses techniques have not realized their true potential. Bioinformatics can be viewed as a field of discovering knowledge from life sciences data with the aid of Information Technology, to find answers to unresolved problems in biology. An example of the benefits of bioinformatics research could be the discovery of new drugs. 

TC20 has interests in pattern recognition problems in the following areas:

       ·        Computational genomics and comparative genomics

      ·        Gene expression analysis and functional genomics

·        Alignment of sequences: DNA, protein, structures, etc.

·        Phylogenic analysis of species, sequences, structures

·        Structural genomics and proteomics

·        Functional and molecular imaging

·        Data mining, data integration, and visualization

·        Information fusion such as combining sequences, expressions, texts, etc.

·        Pathway analysis, gene regulatory networks, etc.

·        Disease modeling.

·        Medical informatics.

 

Pattern Recognition for Bioinformatics:

The information stored in DNA, a chain of four nucleotides (A, T, G, and C), is first converted to mRNA through the process of transcription and then converted to the functional form of life, proteins, through the process of translation. The initiation of translation or transcription process depends on the presence of specific patterns of DNA, RNA, and motifs. Research on detecting specific patterns of DNA sequences such as genes, protein coding regions, and promoters, leads to the better understanding of molecular level function of the cell. Comparative genomics focuses on comparisons across the genomes to find conserved patterns over the evolution, which should possess some functional significance. Construction of evolutionary trees is useful to know how genome and proteome are evolved over all species by ways of a complete library of motifs and genes.

A protein’s functionality or its interaction with another protein is mainly determined by it 3-D structure. Prediction of protein’s 3-D structure from its 1-D amino-acid sequence remains an open problem in structural genomics; protein-protein interactions determine essential functions in living cells. Computational modeling and visualization tools of 3-D structures of proteins help biologists to infer cellular activities.

The challenge in functional genomics is to analyze gene expression data accumulated by microarray techniques to discover the clusters of co-regulated genes and thereby gene regulatory networks, leading to the understanding of regulatory mechanisms of genes and pathways. Molecular imaging provides techniques for in vivo sensing and imaging of molecular events, which measure biological processes in living organism at the molecular and cellular level.  The techniques to fuse and integrate different kinds of information derived from different life science data are yet to realize its full potential.

The ever expanding knowledge of biomedical and phenotype data, combined with genotypes, is becoming difficult to be analyzed by traditional text-based methods. Advanced data mining techniques, where the use of ontologies 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.

The goal of 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. One of the goals is to facilitate and bring together the pattern recognition and the life science communities.

 
Upcoming Activities:

  • The 4th International Pattern Recognition Summer School will be organized in Plymouth, UK between 23-28 July, 2006:

http://www.patternrecognitionschool.com

  • First Workshop on Pattern Recognition in Bioinformatics (PRIB’06) will be held in conjunction with ICPR 2006 in Hong Kong:

http://www.ntu.edu.sg/sce/prib06/

 

Publications:

·     IAPR Newsletter article, Volume 27, Number 2, April 2005

·     Interim report, June 2005


Membership List:

·        Raj Acharya, The Pennsylvania State University, USA, (Chair)
acharya@cse.psu.edu

·        Fransisco Azuaje, University of Ulster, Northern Ireland
fj.azuaje@ulster.ac.uk

·        Vladimir Brusic, University of Queensland, Australia
vladimir@i2r.a-star.edu.sg

·        Phoebe Chen, Deakin University, Australia
phoebe.chen@deakin.edu.sg

·       David Corne, University of Exeter, United Kingdom

       D.W.Corne@ex.ac.uk

·        Marchiori Elena, Vrije Universiteit Amsterdam, The Netherlands
elena@cs.vu.nl

·        Mariofanna Milanova, University of Arkansas at Little Rock, USA
mgmilanova@ualr.edu

·        Gary Fogel, Natural Selection, Inc., USA
gfogel@natural-selection.com

·        Saman Halgamuge, University of Melbourne, Australia
saman@unimelb.edu.au

·        Visakan Kadirkamanathan, The University of Scheffield, United Kingdom
visakan@sheffield.ac.uk

·        Nik Kasabov, Auckland University of Technology, New Zealand
nkasabov@aut.ac.nz

·        Nikhil R. Pal, Indian Statistical Institute, India
nikhil@isical.ac.in

·        Muthu Palaniswami, The University of Melbourne, USA
swami@ee.mu.oz.au

·        Jagath C. Rajapakse, Nanyang Technological University, Singapore (Vice Chair)
asjagath@ntu.edu.sg

·        Gwenn Volkert, Kent State University, USA
volkert@cs.kent.edu

·        Roy E. Welsch, Massachusetts Institute of Technology, USA
rwelsch@mit.edu

·        Kay C. Wiese, Simon Fraser University, Canada
wiese@sfu.ca

·        Limsoon Wong, National University of Singapore, Singapore
limsoon@i2r.a-star.edu.sg

·        Jerry Wu, Wellcome Trust Sanger Institute, United Kingdom

jerry.wu@sanger.ac.uk

·        Qiang Yang, Hong Kong University of Science and Technology, Hong Kong
qyang@cs.ust.hk

·        Yanqing Zhang, Georgia State University, USA
yzhang@cs.gsu.edu

·        Irwin King, Chinese University of Hong Kong, Hong Kong
king@cse.cuhk.edu.hk

·        Alexey V. Kochetov, Russian Academy of Sciences, Russia
ak@bionet.nsc.ru

·        Graham Leedam, Nanyang Technological University, Singapore
ASGLEEDHAM@ntu.edu.sg

·        Ajit Narayanan, University of Exeter, United Kingdom
A.Narayanan@ex.ac.uk