Pattern Recognition in Bioinformatics

1. Front Matter

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2. Session 1: Clustering

   1. A New Framework for Co-clustering of Gene Expression Data

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

      Pages 1-12

   2. Biclustering of Expression Microarray Data Using Affinity Propagation

      • Alessandro Farinelli, Matteo Denitto, Manuele Bicego

      Pages 13-24

   3. A Two-Way Bayesian Mixture Model for Clustering in Metagenomics

      • Shruthi Prabhakara, Raj Acharya

      Pages 25-36

   4. CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA

      • Zejun Zheng, Thuy-Diem Nguyen, Bertil Schmidt

      Pages 37-49

3. Session 2: Biomarker Selection and Classification (1)

   1. New Gene Subset Selection Approaches Based on Linear Separating Genes and Gene-Pairs

      • Amirali Jafarian, Alioune Ngom, Luis Rueda

      Pages 50-62

   2. Identification of Biomarkers for Prostate Cancer Prognosis Using a Novel Two-Step Cluster Analysis

      • Xin Chen, Shizhong Xu, Yipeng Wang, Michael McClelland, Zhenyu Jia, Dan Mercola

      Pages 63-74

   3. Renal Cancer Cell Classification Using Generative Embeddings and Information Theoretic Kernels

      • Manuele Bicego, Aydın Ulaş, Peter Schüffler, Umberto Castellani, Vittorio Murino, André Martins et al.

      Pages 75-86

4. Session 3: Network Inference and Analysis (1)

   1. Integration of Epigenetic Data in Bayesian Network Modeling of Gene Regulatory Network

      • Jie Zheng, Iti Chaturvedi, Jagath C. Rajapakse

      Pages 87-96

   2. Metabolic Pathway Inference from Time Series Data: A Non Iterative Approach

      • Laura Astola, Marian Groenenboom, Victoria Gomez Roldan, Fred van Eeuwijk, Robert D. Hall, Arnaud Bovy et al.

      Pages 97-108

   3. Highlighting Metabolic Strategies Using Network Analysis over Strain Optimization Results

      • José Pedro Pinto, Isabel Rocha, Miguel Rocha

      Pages 109-120

5. Session 4: Biomarker Selection and Classification (2)

   1. Wrapper- and Ensemble-Based Feature Subset Selection Methods for Biomarker Discovery in Targeted Metabolomics

      • Holger Franken, Rainer Lehmann, Hans-Ulrich Häring, Andreas Fritsche, Norbert Stefan, Andreas Zell

      Pages 121-132

   2. Ensemble Logistic Regression for Feature Selection

      • Roman Zakharov, Pierre Dupont

      Pages 133-144

   3. Gene Selection in Time-Series Gene Expression Data

      • Prem Raj Adhikari, Bimal Babu Upadhyaya, Chen Meng, Jaakko Hollmén

      Pages 145-156

   4. Multi-task Drug Bioactivity Classification with Graph Labeling Ensembles

      • Hongyu Su, Juho Rousu

      Pages 157-167

6. Session 5: Image Analysis (1)

   1. A Bilinear Interpolation Based Approach for Optimizing Hematoxylin and Eosin Stained Microscopical Images

      • Kaya Kuru, Sertan Girgin

      Pages 168-178

   2. Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods

      • Olivier Stern, Raphaël Marée, Jessica Aceto, Nathalie Jeanray, Marc Muller, Louis Wehenkel et al.

      Pages 179-190

7. Session 6: Biomarker Selection and Classification (3)

   1. A New Gene Selection Method Based on Random Subspace Ensemble for Microarray Cancer Classification

      • Giuliano Armano, Camelia Chira, Nima Hatami

      Pages 191-201

   2. A Comparison on Score Spaces for Expression Microarray Data Classification

      • Alessandro Perina, Pietro Lovato, Marco Cristani, Manuele Bicego

      Pages 202-213

   3. Flux Measurement Selection in Metabolic Networks

      • Wout Megchelenbrink, Martijn Huynen, Elena Marchiori

      Pages 214-224

This book constitutes the refereed proceedings of the 6th International Conference on Pattern Recognition in Bioinformatics, PRIB 2011, held in Delft, The Netherlands, in November 2011. The 29 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers cover the wide range of possible applications of bioinformatics in pattern recognition: novel algorithms to handle traditional pattern recognition problems such as (bi)clustering, classification and feature selection; applications of (novel) pattern recognition techniques to infer and analyze biological networks and studies on specific problems such as biological image analysis and the relation between sequence and structure. They are organized in the following topical sections: clustering, biomarker selection and classification, network inference and analysis, image analysis, and sequence, structure, and interactions.

• gene regulatory network
• kernel methods
• metabolic engineering
• protein structure prediction
• support vector machines
• algorithm analysis and problem complexity

• Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands

  Marco Loog, Marcel J. T. Reinders, Dick Ridder

• Netherlands Cancer Institute, Bioinformatics and Statistics, Amsterdam, The Netherlands

  Lodewyk Wessels

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