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Executive Board
Raj Acharya Co-Chair
Shandar
Ahamad Information
Madhu
Chetty Chair
Alioune Ngom Publicity
Jagath C.
Rajapakse PRIB Steering
Committee
TC-20
Committee Members
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.
In order to achieve its goal, TC-20
will expand its activities in the following areas:
Education:
through the website, educational materials such as lecture notes,
tutorials, etc., will be made available.
Research: a
database of bioinformatics applications, literature, tools, and benchmark
datasets will be maintained.
Events: organize
PRIB conferences, special sessions at conferences, special issues of
journals, and competitions, etc.
Background
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.
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.
Research Interests
Pattern
recognition in the following areas:
- Computational and
comparative genomics
- Functional genomics
- Structural genomics
and proteomics
- Cheminformatics,
chemigenomics
- Systems biology,
pathway analysis
- Synthetic biology
- Immunoinformatics
- Pharmacogenomics, drug
discovery
- Medical informatics
- Biological imaging
Pattern
Recognition for Bioinformatics
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 subcellular 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.
Annual
Reports
2008-09;
2007-08;
2006-07; 2005-06; 2004-05
PRIB Conference
International Conference on Pattern Recognition in Bioinformatics (PRIB) is
the major event of TC-20.
– PRIB 2010, Nijmegen, The Netherlands
–
PRIB 2009,
Sheffield, UK
–
PRIB
2008, Melbourne, Australia
–
PRIB 2007, Singapore
–
PRIB 2006, Hong Kong, China
PRIB Conferences are held under the guidance of PRIB Steering Committee.
Your proposal to hold PRIB in 2011 should be sent to the PRIB
Secretariat and will be evaluated by the Steering Committee.
The PRIB Committee membership is open to the participants of PBIR
conferences. Enquiries on IAPR TC-20 or PRIB Membership should be directed
to the PRIB Secretariat.
PRIB
Committee Members
Special Sessions
WCCI
– IJCNN 2008
–
Analysis of gene and protein expression data
–
Prediction of protein structures and features
Publications
·
Special
Issue on “Pattern
Discovery in Bioinformatics”, IEEE
Engineering in Medicine and Biology Magazine, Vol. 28, No. 4,
July/August Issue, 2009; Guest Editor: J. C. Rajapakse
·
IAPR
Newsletter, Volume 30, Number 1, January 2008
·
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
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