Sakakibara, Yasubumi.
Genome Informatics 2009. - 1 online resource (243 pages)
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 &.
9781848165632
Electronic books.
Genome Informatics 2009. - 1 online resource (243 pages)
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 &.
9781848165632
Electronic books.
