Pattern Recognition in Bioinformatics
6th IAPR International Conference, PRIB 2011, Delft, The Netherlands, November 2-4, 2011, Proceedings
Published on 29. October 2011
XII, 344 pages
978-3-642-24855-9 (ISBN)
Description
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.
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Marco Loog | Lodewyk Wessels | Marcel J.T. Reinders
Pattern Recognition in Bioinformatics
6th IAPR International Conference, PRIB 2011, Delft, The Netherlands, November 2-4, 2011, Proceedings
Book
10/2011
Springer
€53.49
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Content
- 7036
- Preface
- Organization
- Table of Contents
- Session 1: Clustering
- A New Framework for Co-clustering of Gene Expression Data
- Introduction
- A New Generic Framework for Co-clustering
- Background of Tensor Operations
- The Optimization Model of the Co-clustering Problem
- A Generic Algorithm for Co-clustering
- Algorithm for Co-clustering 2D Matrix Data
- Experimental Results
- Implementation Details and Some Discussions
- Testing Results Using Microarray Datasets
- Testing Using 3D Synthesis Dataset
- Summary and Future Works
- References
- Biclustering of Expression Microarray Data Using Affinity Propagation
- Introduction
- Background: Affinity Propagation
- The Proposed Approach
- Stability Criterion
- Assembling Biclusters
- Experimental Evaluation
- Conclusions and Future Works
- References
- A Two-Way Bayesian Mixture Model for Clustering in Metagenomics
- Introduction
- Related Work
- Methods
- Bayesian Mixture of Poissons
- Two-Way Bayesian Mixture of Poissons
- Bayesian Mixture of Multinomials
- Results
- Conclusion
- References
- CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA
- Introduction
- PGA Approach and ESPRIT
- CRiSPy
- Parallel k-mer Distance Computation
- Parallel Genetic Distance Computation
- Space-Efficient Hierarchical Clustering
- Results
- Performance Comparison of k-mer Distance Computation
- Performance Comparison of Genetic Distance Computation
- Execution Time of CRiSPy Full Run
- Assessment of Microbial Richness Estimation Accuracy by CRiSPy
- Conclusion
- References
- Session 2: Biomarker Selection and Classification (1)
- New Gene Subset Selection Approaches Based on Linear Separating Genes and Gene-Pairs
- Introduction
- Linear Separability of Gene Expression Datasets
- Feature Ranking Criteria
- LS-Pair Ranking Criterion
- LS-Gene Ranking Criterion
- Gene Subset Selection
- Computational Experiments
- Benchmark Datasets
- Conclusion
- References
- Identification of Biomarkers for Prostate Cancer Prognosis Using a Novel Two-Step Cluster Analysis
- Introduction
- Theory and Method
- Step 1: Unsupervised Cluster Analysis
- Mixture Model of Gene Expression.
- EM Algorithm for Cluster Analysis.
- Step 2: Gene Identification Based on Correlation Analysis
- Application
- Simulation Study
- Real Data Analysis
- Discussion
- References
- Renal Cancer Cell Classification Using Generative Embeddings and Information Theoretic Kernels
- Introduction
- The Tissue Microarray (TMA) Pipeline
- Tissue Micro Arrays
- Image Normalization and Patching
- Segmentation
- Feature Extraction
- The Proposed Nuclei Classification Scheme
- Overview
- Generative Model Training
- Generative Embedding
- Discriminative Classification
- Experiments
- One Model for Both Classes
- One Model Per Class
- Conclusions
- References
- Session 3: Network Inference and Analysis (1)
- Integration of Epigenetic Data in Bayesian Network Modeling of Gene Regulatory Network
- Introduction
- Methods
- Experiments and Results
- Conclusion
- Metabolic Pathway Inference from Time Series Data: A Non Iterative Approach
- Introduction
- Metabolic Network Reconstruction
- Modeling Metabolic Interactions
- Derivation of the Objective Function
- Experiments with Artificial Data
- Experiments with Flavonoid Data
- Conclusions
- Highlighting Metabolic Strategies Using Network Analysis over Strain Optimization Results
- Introduction
- Methods
- Overall Workflow
- Flux Balance Analysis
- Strain Optimization
- Solution Set Pre-processing
- Network Representation and Simulation Filtering
- Network Comparison
- Exclusivity.
- Decision points.
- Inversions.
- Variation Analysis
- Experiments and Results
- Case Study and Experimental Setup
- Results and Discussion
- Main Knockouts and Succinate Production.
- dGDP Consumption.
- Alternative L-Threonine Production.
- Conclusions
- References
- Session 4: Biomarker Selection and Classification (2)
- Wrapper- and Ensemble-Based Feature Subset Selection Methods for Biomarker Discovery in Targeted Metabolomics
- Introduction
- Background
- State of the Art
- Methods
- Plasma Samples
- Data Acquisition
- Feature Subset Selection
- Experimental Design
- Results and Discussion
- Benefit of Feature Subset Selection
- Performance of Feature Subset Selection Procedures
- Ensemble Method
- Discriminative Potential of Compound Classes
- Conclusions
- References
- Ensemble Logistic Regression for Feature Selection
- Introduction
- Feature Selection Methods
- Regularized Logistic Regression
- Ensemble Logistic Regression with Feature Resampling
- Experiments
- Microarray Datasets
- Evaluation Metrics
- Experimental Methodology
- Results
- Conclusion and Future Work
- References
- Gene Selection in Time-Series Gene Expression Data
- Introduction
- Time-Series Gene Expression Dataset
- Methodology of Pipeline
- Normalization of Micro-Array Data
- Gene Expression Analysis
- Swap Randomization
- Test Statistic.
- Null Distribution
- p-Values
- Experiments and Results
- Swap Randomization and Convergence Analysis
- Selection of Genes
- Summary and Conclusions
- References
- Multi-task Drug Bioactivity Classification with Graph Labeling Ensembles
- Introduction
- Ensemble Learning with Max-Margin Conditional Random Field Models on Random Graphs
- Learning Graph Labeling with MMCRF
- Graph Generation for Cancer Cell Lines
- Experiments
- Data and Preprocessing
- Compared Methods
- Experiment Setup and Performance Measures
- Results
- Discussion
- Conclusions
- References
- Session 5: Image Analysis (1)
- A Bilinear Interpolation Based Approach for Optimizing Hematoxylin and Eosin Stained Microscopical Images
- Introduction
- The Method
- Conclusion
- References
- Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods
- Context, Motivation, and Strategy
- Methods
- Extraction and Description of Subwindows
- Model Construction Using Extremely Randomized Tree Ensembles
- Prediction in Test Images
- Related Work
- Experiments
- Datasets
- Evaluation Protocols
- Results and Observations
- Conclusion and Future Work
- References
- Session 6: Biomarker Selection and Classification (3)
- A New Gene Selection Method Based on Random Subspace Ensemble for Microarray Cancer Classification
- Introduction
- Diverse Accurate Feature Selection
- Feature-Space Coverage
- Feature Evaluation and Ranking
- DAFS Algorithm
- Experimental Results for Gene Expression Data
- Dataset Description
- DAFS Setup
- Numerical Experiments
- Conclusions
- References
- A Comparison on Score Spaces for Expression Microarray Data Classification
- Introduction
- Methodology
- Probabilistic Latent Semantic Analysis
- Generative Score-Spaces
- Parameters Based Score Space
- Random Variable Based Methods
- Experimental Evaluation
- Conclusions
- Flux Measurement Selection in Metabolic Networks
- Introduction
- Related Work
- Research Problem
- Methods
- RMEV-G: Reaction Minimizing Expected Volume Greedy
- Experimental Analysis
- Results
- Conclusion and Discussion
- Future Work
- References
- Session 7: Network Inference and Analysis (2)
- Lagrangian Relaxation Applied to Sparse Global Network Alignment
- Introduction
- Preliminaries
- Method
- Solving Strategies
- Experimental Evaluation
- Edge-Correctness
- GO Similarity
- Conclusion
- References
- Stability of Inferring Gene Regulatory Structure with Dynamic Bayesian Networks
- Introduction
- Gene Regulatory Networks
- Dynamic Bayesian Networks
- Maximum Likelihood Estimate of the Structure
- Structure Learning
- Stability
- Simulation of Gene Expression Time-Series
- Stability of Estimation of Structure
- Experiments and Results
- Network Topology
- Performance Evaluation
- Accuracy.
- Stability.
- Conclusions
- References
- Integrating Protein Family Sequence Similarities with Gene Expression to Find Signature Gene Networks in Breast Cancer Metastasis
- Introduction
- Material and Methods
- Data Description
- Protein-Protein Sequence Similarity
- Step 1.
- Step 2.
- Step 3.
- Step 4.
- Subnetworks Construction
- Signature Concordance
- Classification Procedure
- Results and Discussion
- Cross Study Prediction Evaluation
- Functionally Coherent Subnetworks
- Consistent Consensus Genes across the Datasets
- Conclusion
- References
- Session 8: Sequence, Structure, and Interactions
- Estimating the Class Posterior Probabilities in Protein Secondary Structure Prediction
- Introduction
- Multi-class Support Vector Machines
- General Introduction
- Dedication to Sequence-to-Structure Prediction
- Structure-to-Structure Classifiers
- Polytomous Logistic Regression
- Linear Ensemble Methods
- Multi-layer Perceptron
- Experimental Results
- Conclusions and Ongoing Research
- References
- Shape Matching by Localized Calculations of Quasi-Isometric Subsets, with Applications to the Comparison of Protein Binding Patches
- Introduction
- Comparing Proteins and Protein Complexes
- Comparing Protein Binding Patches
- Topological Encoding of Cells Complexes
- Shelling a Cell Complex
- Shelling Binding Patches of Protein Complexes: Outline
- Shelling Protein Binding Patches: Detailed Algorithm
- Comparison Algorithms
- Constraints
- Topological Comparison: TEDt
- Geometric Comparison: Clique
- Hybrid Approach: TEDg
- Relation between the Approaches
- Results: Performances and Scores
- Discussion and Outlook
- References
- Using Kendall-t Meta-Bagging to Improve Protein-Protein Docking Predictions
- Introduction
- Methods and Data
- Training Dataset
- Voronoi Fingerprints and Learning Parameters
- Learning Algorithms
- Bagging
- Finding a Median under the Kendall- Distance
- Results
- Ranking Hex Docking Solutions
- Further Insight on Difficult Targets
- Conclusion
- References
- PAAA: A Progressive Iterative Alignment Algorithm Based on Anchors
- Introduction
- Preliminaries
- Algorithm
- Time Complexity
- An Illustrative Example
- Experimental Study
- Conclusions and Perspectives
- References
- Session 9: Image Analysis (2)
- Heat Diffusion Based Dissimilarity Analysis for Schizophrenia Classification
- Introduction
- Shape Analysis by Heat Diffusion
- Dissimilarities
- Dissimilarity Measures
- Dissimilarity Space
- Random Subspace Method and Adaptation to Dissimilarity Computation
- Dissimilarity Combination
- Experiments
- MRI Data Collection
- Histogram Computation Using HKS
- Experimental Methodology
- Results
- Conclusion
- References
- Epithelial Area Detection in Cytokeratin Microscopic Images Using MSER Segmentation in an Anisotropic Pyramid
- Introduction
- State of the Art
- Aim of the Study
- Image Enhancement
- Crypt Outer Borders Detection
- Anisotropic Diffusion Pyramid
- MSER for Epithelial Area Detection
- Crypts Separation and Lumen Detection
- Crypts Separation
- Lumen Detection
- Results
- Conclusions
- References
- Pattern Recognition in High-Content Cytomics Screens for Target Discovery - Case Studies in Endocytosis
- Introduction
- Methodology
- Image Acquisition
- Image Analysis
- Data Analysis
- Experimental Results
- Dynamic Phenotype Stage
- Phenotype Classification
- Conclusions
- References
- Author Index
