Computational Intelligence Methods for Bioinformatics and Biostatistics

Name: Computational Intelligence Methods for Bioinformatics and Biostatistics | 7th International Meeting, CIBIB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers
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7th International Meeting, CIBIB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers

Riccardo Rizzo Paulo J.G. Lisboa(Herausgeber*in)

Springer (Verlag)

Erschienen am 18. Juli 2011

XIII, 288 Seiten

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Inhalt

Title
Preface
Organization
Table of Contents
Tutorial
Management and Analysis of Protein-to-Protein Interaction Data
Introduction
Generation and Management of Protein-to-Protein Interaction Data
Experimental Methods for PPI Data Generation
Standards for PPI Data
Protein-to-Protein Interaction Databases
Analysis and Comparison of Protein-to-Protein Interaction Networks
Visualization of Large Protein-to-Protein Interaction Networks
Ontology-Based Analysis of Protein-to-Protein Interaction Data
Conclusion and Future Work
References
Plenary Talks
The Three Steps of Clustering in the Post-Genomic Era: A Synopsis
Introduction
Experimental Set-Up
Datasets
Distances
Algorithms and Hardware
Evaluating Distance-Clustering Performance via ROC Analysis
Clustering Solutions and ROC Plane
The Distance between Clustering Solutions in ROC Plane
A Procedure to Compare Distance Functions and Clustering Algorithms Via ROC Analysis
Results
Clustering Algorithms
Internal Validation Measures
FC and Consensus Validation Measures
Conclusions
De Novo Protein Subcellular Localization Prediction by N-to-1 Neural Networks
Introduction
Materials and Methods
Datasets
Predictive Architecture: N1-NN
Evaluating Performance
Results and Discussion
Conclusion and Future Work
References
Sequence Analysis, Promoter Analysis and Identification of Transcription Factor Binding Sites
Osmoprotectants in the Sugarcane (Saccharum spp.) Transcriptome Revealed by in Silico Evaluation
Introduction
Material and Methods
Results
Sugarcane Orthologs
Expression Pattern Analysis of Sugarcane Orthologous
Phylogenetic Analysis
Discussion
Sugarcane Osmoprotectants
Expression Pattern
Dendrograms/Phylogenetic Analysis
Concluding Remarks
References
IP6K Gene Discovery in Plant mtDNA
Introduction
Methods
L-SME
L-SME Settings
Results
Discussion
Conclusion
References
Identification and Expression of Early Nodulin in Sugarcane Transcriptome Revealed by in Silico Analysis
Introduction
Material and Methods
Results
Sugarcane Orthologs
Expression Pattern
Dendrograms
Discussion
Sugarcane Orthologs
Expression Pattern
Dendrograms
Concluding Remarks
References
Methods for the Unsupervised Analysis, Validation and Visualization of Structures Discovered in Bio-molecular Data - Prediction of Secondary and Tertiary Protein Structures
An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice
Introduction
Anatomical Segmentation
Non-linear Correction of Atlas Slices
Registration of an Experimental Slice into the 3D Model of Mouse Brain
Deformation of Virtual Slice with the Aid of Keypoints
Expression Detection and Image Resolution Reduction
Image Preprocessing
Extraction of the High-Resolution Expression Map
Reduction of the Expression Map Resolution
Postprocessing Using Anatomical Segmentation
Discussion
References
Prediction of the Bonding State of Cysteine Residues in Proteins with Machine-Learning Methods
Introduction
Materials and Methods
Dataset Description
Machine-Learning Based Predictors
Input Description
Scoring Indices
Results and Discussion
Evaluation of the Different Methods
Organism-Based Prediction: Eukaryotes vs. Prokaryotes
Conclusions
References
Supervised Classification Methods for Mining Cell Differences as Depicted by Raman Spectroscopy
Introduction
Materials
Dataset
Raman Spectroscope
Data Preprocessing
Methods
Support Vector Machines
Regularized Generalized Eigenvalue Classifier
k-Nearest Neighboor
Linear Discriminant Analysis
Software
Improved Iterative Scaling
Results and Discussion
Classification and Model Selection
Impact of Training Set Dimension
Concluding Remarks
References
Gene Expression Data Analysis
Use of Biplots and Partial Least Squares Regression in Microarray Data Analysis for Assessing Association between Genes Involved in Different Biological Pathways
Introduction
Methods
PCA-Based Biplots and Passive Projection
Partial Least Squares Regression
Results
Description of Dataset
Biplots and Passive Projections
PLS Regression
Discussion
Conclusions and Future Work
References
Qualitative Reasoning on Systematic Gene Perturbation Experiments
Methods
Results
Discussion
References
Biclustering by Resampling
Introduction
The Biclustering Problem
Possibilistic Clustering Paradigm
The Possibilistic Approach to Biclustering
Bootstrap Aggregating (Bagging)
Improved Possibilistic Clustering Algorithm
Applying Bootstrap Aggregating to a PBC Model
Validating Biclusters
Results
The Analysis of the Synthetic Data Matrix
Analysis with the PBC Bagging Method of the Real Data (Yeast)
Conclusion
References
Labeling Negative Examples in Supervised Learning of New Gene Regulatory Connections
Introduction
Approach
Candidate Negative Selection Heuristics
Methods
Random Generation of a Gene-Gene Regulatory Network of G Genes
Generation of Synthetic Microarray Experiments
Random Selection of P Non-self Interactions Which Are Assumed to be Known
Selection of Candidate Negative Examples
Cross Validation of Classification Performance.
Learning Scheme
Discussion
Quality of the Selected Set SH of Negative Examples
Performance of an SVM Classifier Trained with SH
Conclusion
References
MOSCFRA: A Multi-objective Genetic Approach for Simultaneous Clustering and Gene Ranking
Introduction
Multi-objective Optimization
Non-dominated Sorting Genetic Algorithm-II
Clustering
Cluster Validity Index
Proposed Technique
Chromosome Representation and Initial Population
Fitness Computation
Crossover
Mutation
Selection, Elitism and Termination
Final Solution Selection
Experiments and Results
Experimental Design
Result and Discussion
Conclusion and Future Scope
References
Bio-medical Text Mining and Imaging - Methods for Diagnosis and Prognosis
A Multi-relational Learning Framework to Support Biomedical Applications
Introduction
Problem Description
Multi-relational Learning Approach
Multi-relational Clustering
Multi-relational Rule Induction
The Framework
Image-Based Features Extraction
Knowledge Extraction
Evaluation and Discussion
Conclusion and Future Work
References
Data Driven Generation of Fuzzy Systems: An Application to Breast Cancer Detection
Introduction
Related Works
The Proposed Approach
Experimental Results
The Wisconsin Breast Cancer Dataset
Experimental Parameters
Numerical Results
The Selected FIS
Conclusions and Future Directions
References
A Knowledge Based Decision Support System for Bioinformatics and System Biology
Introduction
Background
System Structure
Decision-Making Modules
Implementation Details
Application Scenarios
PPI Networks
GRN Inference
Conclusions
References
Mathematical Modelling and Simulation of Biological Systems
Dynamic Simulations of Pathways Downstream of ERBB-Family: Exploration of Parameter Space and Effects of Its Variation on Network Behavior
Methods
Simulations Using ODEs
Sensitivity / Robustness of Our Network to Perturbations
Results
Molecular Interaction Map Supporting Our Simulations
Sensitivity / Robustness of Our Simulations
Discussion
References
Robustness Analysis of a Linear Dynamical Model of the Drosophila Gene Expression
Introduction
Background: The Model
Methods
Robustness of the Model against Perturbations of the Parameters
Robustness of the Model against Perturbation of the Initial Conditions
Results and Discussion
Robustness against Network Perturbations
Robustness against Perturbation of the Initial Conditions
Conclusion and Outlook
References
Intelligent Clinical Decision Support Systems (i-CDSS)
Intelligent Clinical Decision Support Systems for Non-invasive Bladder Cancer Diagnosis
Introduction
Methods
Results
Patients Data
Preprocessing Results
Conclusions
References
Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
Introduction
Related Work
Dataset
Methodology
Pixel Features
Self-Organizing Maps and K-Means Clustering
Experimental Results
Conclusions
References
Classification of Clinical Gene-Sample-Time Microarray Expression Data via Tensor Decomposition Methods
Introduction
Related Works
Methods
Experiments
Conclusion
References
Author Index

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