Genome Informatics 2009 : : Genome Informatics Series Vol. 23 - Proceedings Of The 20th International Conference.

• Author: Sakakibara, Yasubumi.
• TeilnehmendeR: Morishita, Shinichi.; Lee, Sang Yup.
• Place / Publishing House: Singapore : : World Scientific Publishing Company,, 2009.; ©2009.
• Year of Publication: 2009
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (243 pages)

Table of Contents

• Intro
• CONTENTS
• Preface
• Acknowledgments
• Committees
• Part A Full Papers
• Predicting Protein-Protein Relationships from Literature Using Latent Topics T. Aso €j K. Eguchi
• 1. Introduction
• 2. LDA and Estimation Algorithms
• 2.1. Generative Process of LDA
• 2.2. Collapsed Gibbs Sampling Inference
• 2.3. Collapsed Variational Bayesian Inference
• 3. Protein-Protein Relationship Prediction based on LDA
• 4. Data and Entity Representation
• 4.1. GENIA Collection
• 4.2. TREC Collection and GENIA Tagger
• 5. Experiments
• 5.1. Log-Likelihood
• 5.2. Entity-Link Prediction
• 5.2.1. Experimental Settings
• 5.2.2. Task-based Evaluation
• 5.2.3 . Protein-Protein Relationship Network
• 6. Conclusions
• Acknowledgments
• References
• Evaluation of DNA Intramolecular Interactions for Nucleosome Positioning in Yeast M. Fernandez, S. Fujii, H. Kono €j A. Sarai
• 1. Introduction
• 2. Method and Results
• 2.1. Intramolecular Interaction Energy Calculation
• 2.2. Oscillation Pattern of Dinucleotides Along the Nucleosome Structure
• 2.3. Intramolecular Energy Profile of Yeast Genome
• 3. Discussions
• References
• Quality Control and Reproducibility in DNA Microarray Experiments A. Fujita, J. R. Sato, F. H. L. da Silva, M. C. Galviio, M. C. Sogayar €j S. Miyano
• 1. Introduction
• 2. Materials and Methods
• 2.1. Dahlberg's Error (D.E.)
• 2.2. Support Vector Regression (SVR)
• 2.3. Modeling DNA Microarray Data
• 2.4. DNA Microarray
• 2.4 .1. Cell Lysis and RNA Extraction
• 2.4.2. Labeling and Purification of Targets
• 2.4.3. Hybridization and Washing of the DNA Arrays
• 3. Results and Discussions
• Acknowledgments
• References
• Comparative Analysis of Topological Patterns in Different Mammalian Networks B. Goemann, A. P. Potapov, M. Ante €j E. Wingender
• 1. Introduction
• 2 Methods.
• 2.1 Construction of the Networks
• 2.2 Computation of the Painvise Discollnect
• v
• ty Index
• 3 Results and Discussion
• 3.1 Autoregulation as a Feature of the Most 1mportant Nodes
• 3.2 The Mutual Regulation of Two Nodes is a Motif
• 3.3 Three-Node Patterns in the Networks Analyzed
• 3.4 Largelmportant Subnetworks Derived from Pattern Analysis
• 4 Conclusions
• Acknowledgments
• References
• Tools for Investigating Mechanisms of Antigenic Variation: New Extensions to varDB C. N. Hayes, D. Diez, N. Joannin, M. Kanehisa, M. Wahlgren, C. E. Wheelock €j S. Goto
• 1. Introduction
• 2. Tools for Investigating Mechanisms of Antigenic Variation
• 2.1. Sequence Selection and Preparation
• 2.2. Generating a Codon Alignment
• 2.3 . Analyzing Codon Usage
• 2.4. Nucleotide Repeats and DNA Secondary Structure
• 2.5. Mutation Hotspot Motifs
• 2.6. Recombination
• 2.7. Variability and Immune Selection
• 3. Conclusions
• Acknowledgments
• References
• Localized Suffix Array and Its Application to Genome Mapping Problems for Paired-End Short Reads K. Kimura 8 A. Koike
• 1. Introduction
• 2. Localized Suffix Array (LSA)
• 2.1. Basic Idea
• 2.2. Procedural Introduction of Recursive Localization (RL) and LSA
• 2.3. Algorithms for LSA Construction and RL of Index Intervals
• 3. Application to Paired-End (PE) Mapping Problems
• 3.1. Single-End (SE) Mapping Method
• 3.2. Paired-End (PE) Search Methods
• 3.3. Experimental Results
• 4. Additional Results and Discussions
• 5. Conclusions
• References
• Comparative Analysis of Aerobic and Anaerobic Prokaryotes to Identify Correlation between Oxygen Requirement and Gene-Gene Functional Association Patterns y. Lin 8 H. Wu
• 1. Introduction
• 2. Aerobic and Anaerobic Prokaryotes
• 3. Quantification of Gene-Gene Functional Association
• 3.1. Stochastic Model for Gene Arrangement.
• 3.2. Validation of Gene-Gene Functional Association Measures
• 3.2.1. Validation of the A (gi, gj) Measures based on Biological Process Ontology Annotations
• 3.2.2. Validation of the A (gi, gj ) Measures based on KEGG Pathway Annotations
• 4. Identification of Gene Pairs with Different Functional Association Patterns under the Two Different Oxygen Requirement Conditions
• 4.1. Student's t-Test Results
• 4.2. Biological Implications of the Gene Pairs with Large/Small p- Values
• 5. Prediction of Oxygen Requirement Conditions Based on certain Gene-Gene Functional Association Patterns
• 6. Conclusion
• Acknow ledgments
• References
• Calculation of Protein-Ligand Binding Free Energy Using Smooth Reaction Path Generation (SRPG) Method: A Comparison of the Explicit Water Model, GB/SA Model and Docking Score Function D. Mitomo, Y. Fukunishi, J. Higo 8 H. Nakamura
• 1. Introduction
• 2. Methods and Materials
• 2.1. ..1G Calculation
• 2.2. Ligand Dissociation Path
• 2.3. Smooth Reaction Path
• 2.4. PMF Calculation
• 2.5. Computational Models
• 3. Results
• 4. Discussion
• 5. Conclusion
• Acknowledgments
• References
• Structural Insights into the Enzyme Mechanism of a New Family of D-2-Hydroxyacid Dehydrogenases, a Close Homolog of 2-Ketopantoate Reductase S. Mondal 8 K. Mizuguchi
• 1. Introduction
• 2. Material and Methods
• 2.1. Comparative Modeling and Structural Analysis
• 2.2. Normal Mode Analysis
• 3. Results
• 3.1. Comparative Modeling
• 3.2. Hinge Bending
• 3.3. Cofactor Recognition
• 3.4. Substrate Recognition
• 4. Discussion
• 5. Conclusions
• Acknowledgments
• References
• Comprehensive Analysis of Sequence-Structure Relationships in the Loop Regions of Proteins S. Nakamura 8 K. Shimizu
• 1. Introduction
• 2. Materials and Methods
• 2.1. Preparation of Datasets
• 2.2. Predictions Using SVR.
• 2.3. Calculation of Prediction Accuracy
• 2.4. Random Prediction
• 2.5. Dataset from GASP8 Targets
• 3. Results and Discussion
• 4. Conclusion
• References
• The Prediction of Local Modular Structures in a Co-Expression Network Based on Gene Expression Datasets Y. Ogata, N. Sakurai, H. Suzuki, K. Aoki, K. Saito 8 D. Shibata
• 1. Introduction
• 2. Method and Results
• 2.1. Definitions
• 2.2. Microarray datasets
• 2.3. An algorithm to extract co-expression modules
• 2.4. Testing
• 2.5. Implementation
• 3. Discussion
• 4. Conclusions
• Acknowledgments
• References
• Gradient-Based Optimization of Hyperparameters for Base-Pairing Profile Local Alignment Kernels K. Sato, Y. Saito 8 Y. Sakakibara
• 1. Introduction
• 2. Methods
• 2.1. Base-Pairing Profile Local Alignment Kernels
• 2.2. Gradient-Based Optimization for SVMs
• 3. Results
• 4. Discussion
• 5. Conclusion
• Acknowledgments
• References
• A Method for Efficient Execution of Bioinformatics Workfiows 1. Seo, Y. Kido, S. Seno, Y. Takenaka 8 H. Matsuda
• 1. Introduction
• 2. Workflow Operations in Hybrid Architecture
• 3. Improved Method
• 4. Experimental Result
• 4.1. Experiment with Test Web Services
• 4.2. Experiment with Bioinformatics Web Services in Distributed Environment
• 5. Discussion
• References
• Development of a New Meta-Score for Protein Structure Prediction from Seven All-Atom Distance Dependent Potentials Using Support Vector Regression M. Shirota, T. Ishida 8 K. Kinoshita
• 1. Introduction
• 2. Materials and Methods
• 2.1. Decoy Sets
• 2.2. Quality of the Structure
• 2.3. Component Statistical Potentials
• 2.4. Normalization of the All-Atom Distance Dependent Potentials
• 2.5. Development of the Meta-Score
• 2.6. Assessment of Potentials
• 2.7. Statistical Significance of the Difference in Performance
• 3. Results and Discussion.
• 3.1. Performances for the Training Set
• 3.2. Performances for the Test Set
• 3.3. Evaluation of the Meta-Score as an Absolute Quality Score for Protein Structures
• 4. Conclusion
• Acknowledgments
• References
• Refining Markov Clustering for Protein Complex Prediction by Incorporating Core-Attachment Structure S. Srihari, K. Ning fj H. W. Leong
• 1. Introduction
• 2. Methods
• 2.1. Clustering the PPI Graph Using MCL
• 2.2. Determining Core Proteins
• 2.3. Filtering Out Noisy Clusters
• 2.4. Determining Attachment Proteins
• 2.5. Determining Module Proteins
• 2.6. Determining Complexes and Ranking them
• 3. Results and Discussions
• 3.1. Improvement over MeL
• 3.2. Comparisons with CORE and COA CH
• 3.3. Analysis of Complexes Predicted by MCL-CA
• 4. Conclusions and Future Work
• Acknowledgments
• References
• An Assessment of Prediction Algorithms for Nucleosome Positioning Y. Tanaka fj K. Nakai
• 1. Introduction
• 2. Materials and Methods
• 2.1. Genome-Scale Nucleosome Maps
• 2.2. Application of Prediction Algorithms
• 2.3. Receiver Operating Characteristic (ROC) Curve
• 2.4. Over- and Under-Represented Oligomers
• 3 Results and Discussion
• 3.1 Prediction Ability 0/ Each Algorithm/or Overall Nucleosomes
• 3.2 General and Specific Sequence Dependencies in Nucleosome Positioning ~ ,'
• 4 Conclusions
• Additional Data and URL
• Acknowledgments
• References
• Cancer Classification Using Single Genes X. Wang fj O. Gotoh
• 1. Introduction
• 2. Materials and Methods
• 2.1. Datasets
• 2.2. Rough Sets
• 2.3. Data Preprocessing, Gene Selection and Classification
• 3. Results
• 3.1. Classification Results
• 3.2. Comparison of Classification Results
• 3.3. Analysis of Results
• 4. Discussion
• References.
• RECOUNT: Expectation Maximization Based Error Correction Tool for Next Generation Sequencing Data E. Wijaya, M. C. Frith, Y. Suzuki &amp.