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Pattern Recognition in Bioinformatics

Third IAPR International Conference, PRIB 2008, Melbourne, Australia, October 15-17, 2008. Proceedings

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Madhu Chetty

    1. Gippsland School of IT, Monash University, Churchill, Australia

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  2. Alioune Ngom

    1. University of Windsor, Canada

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    You can also search for this editor in PubMed   Google Scholar

  3. Shandar Ahmad

    1. National Institute of Biomedical Innovation, Osaka, Japan

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5265)

Part of the book sub series: Lecture Notes in Bioinformatics (LNBI)

Included in the following conference series:

Conference proceedings info: PRIB 2008.

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Table of contents (39 papers)

  1. Front Matter

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  2. Protein: Structure, Function, and Interaction

    1. Sequence Based Prediction of Protein Mutant Stability and Discrimination of Thermophilic Proteins

      • M. Michael Gromiha, Liang-Tsung Huang, Lien-Fu Lai
      Pages 1-12

    2. A Method to Find Sequentially Separated Motifs in Biological Sequences (SSMBS)

      • Chetan Kumar, Nishith Kumar, Sarani Rangarajan, Narayanaswamy Balakrishnan, Kanagaraj Sekar
      Pages 13-27

    3. Predicting SUMOylation Sites

      • Denis C. Bauer, Fabian A. Buske, Mikael Bodén
      Pages 28-40

    4. DFS Based Partial Pathways in GA for Protein Structure Prediction

      • Md Tamjidul Hoque, Madhu Chetty, Andrew Lewis, Abdul Sattar
      Pages 41-53

    5. Evaluation of the Stability of Folding Nucleus upon Mutation

      • Mathieu Lonquety, Zoé Lacroix, Jacques Chomilier
      Pages 54-65

    6. Prediction of Protein Beta-Sheets: Dynamic Programming versus Grammatical Approach

      • Yuki Kato, Tatsuya Akutsu, Hiroyuki Seki
      Pages 66-77

    7. Using Multi-scale Glide Zoom Window Feature Extraction Approach to Predict Protein Homo-oligomer Types

      • QiPeng Li, Shao Wu Zhang, Quan Pan
      Pages 78-86

    8. Extraction of Binding Sites in Proteins by Searching for Similar Local Molecular Surfaces

      • Satoshi Koizumi, Keisuke Imada, Tomonobu Ozaki, Takenao Ohkawa
      Pages 87-97

  3. Learning, Classification, and Clustering

    1. A Clustering Based Hybrid System for Mass Spectrometry Data Analysis

      • Pengyi Yang, Zili Zhang
      Pages 98-109

    2. A Modified Markov Clustering Approach for Protein Sequence Clustering

      • Lehel Medvés, László Szilágyi, Sándor M. Szilágyi
      Pages 110-120

    3. Feature Selection and Classification for Small Gene Sets

      • Gregor Stiglic, Juan J. Rodriguez, Peter Kokol
      Pages 121-131

    4. Pseudoknot Identification through Learning TAGRNA

      • Sahar Al Seesi, Sanguthevar Rajasekaran, Reda Ammar
      Pages 132-143

    5. Support Vector Based T-Score for Gene Ranking

      • Piyushkumar A. Mundra, Jagath C. Rajapakse
      Pages 144-153

    6. Prediction of Transcription Factor Families Using DNA Sequence Features

      • Ashish Anand, Gary B. Fogel, Ganesan Pugalenthi, P. N. Suganthan
      Pages 154-164

    7. g-MARS: Protein Classification Using Gapped Markov Chains and Support Vector Machines

      • Xiaonan Ji, James Bailey, Kotagiri Ramamohanarao
      Pages 165-177

  4. Bio-Molecular Networks and Pathways Analysis

    1. Domain-Domain Interaction Identification with a Feature Selection Approach

      • Xing-Ming Zhao, Luonan Chen
      Pages 178-186

    2. Dividing Protein Interaction Networks by Growing Orthologous Articulations

      • Pavol Jancura, Jaap Heringa, Elena Marchiori
      Pages 187-200

    3. Constraint Minimization for Efficient Modeling of Gene Regulatory Network

      • Ramesh Ram, Madhu Chetty, Dieter Bulach
      Pages 201-213

    4. Fusion of Gene Regulatory and Protein Interaction Networks Using Skip-Chain Models

      • Iti Chaturvedi, Jagath C. Rajapakse
      Pages 214-224
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  1. Pattern Recognition in Bioinformatics

Keywords

About this book

In the post-genomic era, a holistic understanding of biological systems and p- cesses,inalltheircomplexity,is criticalincomprehendingnature’schoreography 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 ?eld. With the ever-increasing application of lar- scalehigh-throughputtechnologies,suchasgeneorproteinmicroarraysandmass spectrometry methods, the enormous body of information is growing rapidly. Bioinformaticians are posed with a large number of di?cult problems to solve, arising not only due to the complexities in acquiring the molecular infor- tion but also due to the size and nature of the generated data sets and/or the limitations of the algorithms required for analyzing these data. Although the ?eld of bioinformatics is still in its embryonic stage, the recent advancements in computational and information-theoretic techniques are enablingus to c- ductvariousinsilicotestingandscreeningofmanylab-basedexperimentsbefore these are actually performed in vitro or in vivo. These in silico investigations are providing new insights for interpretation and establishing a new direction for a deeper understanding. Among 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 - going bioinformatics revolution may, in future, inevitably play a major role in many aspects of medical practice and/or the discipline of life sciences.

Editors and Affiliations

  • Gippsland School of IT, Monash University, Churchill, Australia

    Madhu Chetty

  • University of Windsor, Canada

    Alioune Ngom

  • National Institute of Biomedical Innovation, Osaka, Japan

    Shandar Ahmad

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