Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011, Proceedings
Published on 27. April 2011
X, 183 pages
978-3-642-20389-3 (ISBN)
Description
This book constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 events. The 12 revised full papers presented together with 7 poster papers were carefully reviewed and selected from numerous submissions. All papers included topics of interest such as biomarker discovery, cell simulation and modeling, ecological modeling, fluxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, and systems biology.
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Clara Pizzuti | Marylyn D. Ritchie | Mario Giacobini
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011, Proceedings
Book
04/2011
1st Edition
Springer
€53.49
Shipment within 7-9 days
Content
- Title
- Preface
- Organization
- Table of Contents
- Effect of Using Varying Negative Examples in Transcription Factor Binding Site Predictions
- Introduction
- Background
- Description of Data
- Genomic Data
- Problems with the Data and Solutions
- Methods
- Pre-processing: Preparing Training and Test Set
- The Classifier and Its Performance Measures
- Post-processing (Filtering)
- Cross-Validation
- Results and Discussion
- Experiment 1- Using Negative Examples Sequences Not Annotated as TFBSs
- Experiment 2- Replacing Negative Examples with Distal Negative Examples
- Experiment 3- Replacing Negative Examples with Randomized Negative Examples
- Conclusion
- References
- A New Evolutionary Gene Regulatory Network Reverse Engineering Tool
- Introduction
- The Proposed Framework: GRNGen
- The IRMA Network
- Experiments
- Conclusions and Future Work
- References
- ML-Consensus: A General Consensus Model for Variable-Length Transcription Factor Binding Sites
- Introduction
- ML-Consensus
- Basic Development
- Incorporating Information Content and Pairwise Correlation in Scoring
- Using Multiple Consensus for One Transcription Factor
- Using a Sorting-Based Heuristic Alignment
- Methods
- Input
- Training and Testing
- Plotting ROC Curves for Configurations
- Statistical Significance of Performance
- Results and Discussion
- Effect of IC and PS
- Effect of Multiple Consensus
- Effect of Alignment
- Conclusion
- References
- Applying Linear Models to Learn Regulation Programs in a Transcription Regulatory Module Network
- Introduction
- Inferring Regulatory Relationships in Module Networks by Linear Models
- Extracting the Critical Contrast of a Condition Clustering
- Using Moderated t-Statistics to Select Differentially Expressed Transcription Factors
- The Regulatory Score for Assigning a Transcription Factor to a Module
- Experimental Results and Discussion
- Dataset and Validation Reference Database
- Results for Regulation of Nitrogen Utilization
- Linear Model versus LeMoNe in the NCR Process
- Results over the Entire Yeast Stress Dataset
- Conclusion and Future Works
- References
- ATHENA Optimization: The Effect of Initial Parameter Settings across Different Genetic Models
- Introduction
- Epistasis in Complex Human Disease
- Optimization of Neural Networks and Symbolic Regression Formulas
- Methods
- Data Simulation
- Data Analysis
- Results
- Discussion
- References
- Validating a Threshold-Based Boolean Model of Regulatory Networks on a Biological Organism
- Introduction
- Threshold-Based Additive Boolean Update Function
- A Regulatory Network with Defined Boolean Update Functions
- Simulations and Results
- Determining the ABA Model's Regime
- Determining the ADA Update Function's Critical Threshold Value
- Analyzing the Overlap of the Different Update Functions
- Conclusions and Future Work
- References
- A Nearest Neighbour-Based Approach for Viral Protein Structure Prediction
- Introduction
- Methods
- Definition of Protein Distance Map
- Training Phase
- Prediction of Protein Distance Maps
- Evaluation of the Efficiency
- Experimentation and Results
- Conclusions and Future Work
- References
- Annotated Stochastic Context Free Grammars for Analysis and Synthesis of Proteins
- Introduction
- Annotated Context Free Grammars
- n-gram Bayesian Text Classifier for Protein Sequences
- Annotated Stochastic Context Free Grammars
- Clustering Techniques
- Quality Threshold Clustering
- Co-Clustering
- Frog AMP Analysis
- Data
- ACFG Results
- ASCFG Results
- Conclusions
- References
- Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm
- Introduction
- Related Work
- Motif Discovery Problem
- Problem Formulation
- Example
- Multiobjective Artificial Bee Colony Algorithm
- ABC Features
- Implementation Details
- Experimental Results
- Comparisons with Other Authors
- Conclusions
- References
- An Evolutionary Approach for Protein Contact Map Prediction
- Introduction
- Methodology
- Statistical Analysis
- Encoding
- Fitness Function
- Genetic Operators
- Experiments and Results
- Conclusions and Future Work
- References
- Multi-Neighborhood Search for Discrimination of Signal Peptides and Transmembrane Segments
- Introduction
- Local Search for Modeling Amino Acid Insertion Curves
- Biological Knowledge
- Local Search for in Silico Determining the Curves
- Benchmark Dataset and Representation of the Data
- Construction of a Benchmark Dataset: SWP-v2
- Influence of TM Segment Representation
- Statistical Data Analysis
- Evaluation on Other Test Datasets
- New Search Space of Insertion Indexes for Amino Acids
- Principle
- Influence of the Initial Scale
- Multi-Neighborhood Search
- Neighborhoods
- Move Strategy and Multi-Neighborhood Exploration
- Experiments
- Conclusion
- References
- Approximation of Graph Kernel Similarities for Chemical Graphs by Kernel Principal Component Analysis
- Introduction
- Materials and Methods
- Kernels for Chemical Data Mining
- Kernel Principal Component Analysis
- Data Mining with Kernel PCA Vectors
- Experimental Setup
- Results
- Discussion and Conclusion
- References
- Posters
- Experimental Approach for Bacterial Strains Characterization
- Introduction
- Problem Study
- Resolution Methods
- Experimental Results
- Conclusion
- References
- Do Diseases Spreading on Bipartite Networks Have Some Evolutionary Advantage?
- Introduction
- Epidemiological Network Models
- Bipartite Networks
- Pathogen Spreading on Networks: The Importance of Being Bipartite
- Conclusions and Future Works
- References
- Genetic Algorithm Optimization of Force Field Parameters: Application to a Coarse-Grained Model of RNA
- Introduction
- Optimization Problem
- Genetic Algorithm Approach
- Computations
- Conclusions and Future Work
- References
- A Decision Tree-Based Method for Protein Contact Map Prediction
- Introduction
- Materials and Methods
- Data Bases
- Contact Maps Definition
- Model Architecture
- Evaluation of the Efficiency
- Results
- Conclusions
- References
- A Comparison of Machine Learning Methods for the Prediction of Breast Cancer
- Introduction
- Methods
- Data
- Tools, Techniques and Parameters
- Results and Discussion
- Quality Assessment
- Interpretability of Solutions and Feature Selection
- Conclusions and Future Work
- References
- An Automatic Identification and ResolutionSystem for Protein-Related Abbreviations in Scientific Papers
- Introduction
- Related Work
- Abbreviation Identification and Resolution
- Identification Phase
- Resolution Phase
- Experimental Results
- Conclusions
- References
- Protein Complex Discovery from Protein Interaction Network with High False-Positive Rate
- Introduction
- Method
- Data Description
- GECSS(Gene Expression Condition Set Similarity)
- Algorithm
- Experimental Results
- Conclusion
- References
- Author Index
