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Description of application areas

Statistical, structural, and syntactic pattern recognition, neural networks, computational intelligence, graphical and data mining techniques, and their hybrids have found many applications in computational biology and bioinformatics. Such applications include:
•        Analyses of bio-sequences
•        Gene expression analysis and functional genomics
•        Phylogenic analysis of species, sequences, structures
•        Structural genomics and proteomics
•        Information fusion such as combining sequences, expressions, texts, and images, etc.
•        Systems biology: pathway analysis, gene regulatory networks, etc.
•        Disease modeling
•        Medical informatics
•        Biological imaging: functional, molecular, and cellular imaging

Examples of successful projects

A list of some projects (title and name of TC20 member acting as (co) Principal Investigator (PI)) is given below.

•       A century of variability in Greenland melting and iceberg calving, NERC Grant:  (co-PI: Kadirkamanathan, 2011-14)

•       System identification and information processing for complex systems, EPSRC Platform Grant: (co-PI:   Kadirkamanathan,, 2010-15)

•       Investigation of the control mechanism on co-culture bioethanol producing system, Royal Society Collaboration Grant (co-PI: Kadirkamanathan, 2010-12)

•       Modeling, validation, and analysis of gene regulatory networks with delays
•       Ministry of Education (MOE), Singapore (PI: Rajapakse)

•       Statistical study of data from EDEN study SMART Infectious Disease IRG grant, SMART Center, Singapore (PI: Rajapakse)

•       BioSystems and micromechanics IRG grant, SMART Center, Singapore (PI: Rajapakse)

•       Pennsylvania Cancer Alliance Data Warehouse for Cancer Bio-Geo Informatics (PI: Acharya)

•       Algorithm for metagenome assembly (PI: Acharya)

•       Spatio-temporal Organization of Nuclear Structure (PI: Acharya)

•       Analysis of temporal gene expression data (PI: Acharya)

•       Information Fusion and Data combination for Functional Genomics (PI: Acharya)

•       Modelling therapeutic interventions in mitochondrial bioenergetics (member: Marchiori, see http://www.cs.ru.nl/~elenam/csb-bioenergetic.html)

Demos

Members of the TC-20 develop (publicly available) tools and software, see, e.g.

Inference Group Software (Mark Girolami)
http://www.dcs.gla.ac.uk/inference/software.cfm

Publicly available software (Guido Sanguinetti)
http://homepages.inf.ed.ac.uk/gsanguin/software.html

Publicly available software (Bertil Schmidt)
http://www.staff.uni-mainz.de/schmi033/

Publicly available tools and programs (Elena Marchiori)
http://www.cs.ru.nl/~elenam/#Tools and Programs

Reference resources (datasets, evaluation tools)

Members of the TC-20 contribute at individual level to the dissemination of results and datasets. These include the following reference resources.

Dana-Farber Repository for Machine Learning in Immunology
Name: DFRMLI
Type: Resource
Link: http://bio.dfci.harvard.edu/DFRMLI/
Description: Dana-Farber Repository for Machine Learning in Immunology bridges the gap between immunological and computer science/machine learning communities by providing preprocessed and scaled immunological data sets suitable for use in machine learning applications. These datasets include major publically available data sets (from IEDB, CBS, and our group) as well as carefully selected independent validation data sets. The recommendations for scaling and comparison of performance of prediction systems are included in the system too. Some of the data sets in the DFRMLI were used for an earlier machine learning competition.
Publication: Zhang GL, Lin HH, Keskin DB, Reinherz EL, Brusic V. (2011) Dana-Farber repository for machine learning in immunology. J Immunol Methods. 2011; 374(1-2):18-25.

First Machine learning competition in immunology
Name: MLI 2009
Type: Competition
Link: http://bio.dfci.harvard.edu/DFRMLI/HTML/MHCBindingPeptides.php
Description: The competition was held in conjunction with 19th International Conference on Artificial Neural Networks, 14-17 September 2009, Limassol, Cyprus. The project was completed - the results were published in 2011.

Publication: Zhang GL, Lin HH, Keskin DB, Reinherz EL, Brusic V. (2011) Dana-Farber repository for machine learning in immunology. J Immunol Methods. 2011; 374(1-2):18-25.

Second Machine learning competition in immunology
Name: MLI 2012
Type: Competition
Link: http://bio.dfci.harvard.edu/DFRMLI/HTML/natural.php
Description: The competition was held in conjunction with InCoB 2012 and ICIW 2012. The project is ongoing- the results will be published in 2013)
Data sets: http://bio.dfci.harvard.edu/DFRMLI/HTML/natural.php

Yue Wang, Guimei Liu, Mengling Feng, Limsoon Wong. "An Empirical Comparison of Several Recent Epistatic Interaction Detection Methods". Bioinformatics, 27(21):2936--2943, November 2011. Corrigendum. Bioinformatics, 28(1):147--148, January 2012.  (This is an independent benchmarking of several epistatic interaction mining methods.)

Limsoon Wong. "Using Biological Networks in Protein Function Prediction and Gene Expression Analysis". Internet Mathematics, 7(4):274--298, December 2011.  (This is an introductory critical review on network-based gene expression analysis.)

Wilson Goh, Yie Hou Lee, Maxey Chung, Limsoon Wong. "How advancement in biological network analysis methods empowers proteomics". Proteomics, 12(4-5):550--563, February 2012. (This is an introductory critical review on network-based proteomic profile analysis.)