Pattern Recognition in Bioinformatics

1. Front Matter

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2. Generic Methods – I

   1. Robust Community Detection Methods with Resolution Parameter for Complex Detection in Protein Protein Interaction Networks

      • Twan van Laarhoven, Elena Marchiori

      Pages 1-13

   2. Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression

      • Pedro J. Ballester

      Pages 14-25

   3. A Genetic Algorithm for Scale-Based Translocon Simulation

      • Sami Laroum, Béatrice Duval, Dominique Tessier, Jin-Kao Hao

      Pages 26-37

   4. A Framework of Gene Subset Selection Using Multiobjective Evolutionary Algorithm

      • Yifeng Li, Alioune Ngom, Luis Rueda

      Pages 38-48

3. Generic Methods – II

   1. Multiple Tree Alignment with Weights Applied to Carbohydrates to Extract Binding Recognition Patterns

      • Masae Hosoda, Yukie Akune, Kiyoko F. Aoki-Kinoshita

      Pages 49-58

   2. A Unified Adaptive Co-identification Framework for High-D Expression Data

      • Shuzhong Zhang, Kun Wang, Cody Ashby, Bilian Chen, Xiuzhen Huang

      Pages 59-70

   3. Protein Clustering on a Grassmann Manifold

      • Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui

      Pages 71-81

4. Visualization, Image Analysis, and Platforms

   1. Improving the Portability and Performance of jViz.RNA – A Dynamic RNA Visualization Software

      • Boris Shabash, Kay Wiese, Edward Glen

      Pages 82-93

   2. A Novel Machine Learning Approach for Detecting the Brain Abnormalities from MRI Structural Images

      • Lavneet Singh, Girija Chetty, Dharmendra Sharma

      Pages 94-105

   3. An Open Framework for Extensible Multi-stage Bioinformatics Software

      • Gabriel Keeble-Gagnère, Johan Nyström-Persson, Matthew I. Bellgard, Kenji Mizuguchi

      Pages 106-117

5. Applications of Pattern Recognition Techniques

   1. An Algorithm to Assemble Gene-Protein-Reaction Associations for Genome-Scale Metabolic Model Reconstruction

      • João Cardoso, Paulo Vilaça, Simão Soares, Miguel Rocha

      Pages 118-128

   2. A Machine Learning and Chemometrics Assisted Interpretation of Spectroscopic Data – A NMR-Based Metabolomics Platform for the Assessment of Brazilian Propolis

      • Marcelo Maraschin, Amélia Somensi-Zeggio, Simone K. Oliveira, Shirley Kuhnen, Maíra M. Tomazzoli, Ana C. M. Zeri et al.

      Pages 129-140

   3. Principal Component Analysis for Bacterial Proteomic Analysis

      • Y. -h. Taguchi, Akira Okamoto

      Pages 141-152

6. Protein Structure and Docking

   1. Application of the Multi-modal Relevance Vector Machine to the Problem of Protein Secondary Structure Prediction

      • Nikolay Razin, Dmitry Sungurov, Vadim Mottl, Ivan Torshin, Valentina Sulimova, Oleg Seredin et al.

      Pages 153-165

   2. Cascading Discriminant and Generative Models for Protein Secondary Structure Prediction

      • Fabienne Thomarat, Fabien Lauer, Yann Guermeur

      Pages 166-177

   3. Improvement of the Protein–Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: An Application to Interaction Pathway Analysis

      • Masahito Ohue, Yuri Matsuzaki, Takashi Ishida, Yutaka Akiyama

      Pages 178-187

   4. Representation of Protein Secondary Structure Using Bond-Orientational Order Parameters

      • Cem Meydan, Osman Ugur Sezerman

      Pages 188-197

7. Complex Data Analysis

   1. Diagnose the Premalignant Pancreatic Cancer Using High Dimensional Linear Machine

      • Yifeng Li, Alioune Ngom

      Pages 198-209

   2. Predicting V(D)J Recombination Using Conditional Random Fields

      • Raunaq Malhotra, Shruthi Prabhakara, Raj Acharya

      Pages 210-221

This book constitutes the refereed proceedings of the 7th International Conference on Pattern Recognition in Bioinformatics, PRIB 2012, held in Tokyo, Japan, in November 2012. The 24 revised full papers presented were carefully reviewed and selected from 33 submissions. Their topics are widely ranging from fundamental techniques, sequence analysis to biological network analysis. The papers are organized in topical sections on generic methods, visualization, image analysis, and platforms, applications of pattern recognition techniques, protein structure and docking, complex data analysis, and sequence analysis.

• evolutionary algorithms
• extreme machine learning
• genetic algorithm
• protein structure prediction
• supervised classification
• algorithm analysis and problem complexity

• Institute of Medical Science, University of Tokyo, Minato-ku, Japan

  Tetsuo Shibuya

• Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Japan

  Hisashi Kashima

• Department of Comouter Science, Tokyo Institute of Technology, Meguro-ku, Japan

  Jun Sese

• Bioinformatics Project, National Institute of Biomedical Innovation, Suita, Japan

  Shandar Ahmad

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