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Proceedings of the 6th Asia-Pacific Bioinformatics Conference.
High-throughput sequencing and functional genomics technologies have given us the human genome sequence as well as those of other experimentally, medically, and agriculturally important species, thus enabling large-scale genotyping and gene expression profiling of human populations. Databases contai...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
World Scientific Publishing Company,
2007.
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| Colección: | Series on advances in bioinformatics and computational biology.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; APBC 2008 Organization; Program Committee; Additional Reviewers; Keynote Papers; Recent Progress in Phylogenetic Combinatorics Andreas Dress; 1. Background; 2. Discussion; References; KEGG for Medical and Pharmaceutical Applications Minoru Kanehisa; Protein Interactions Extracted from Genomes and Papers Alfonso Valencia; Contributed Papers; String Kernels with Feature Selection for SVM Protein Classification Wen-Yun Yang and Bao-Liang Lu; 1. Introduction; 2. A string kernel framework; 2.1. Notations; 2.2. Pramework definition; 2.3. Relations with existing string kernels.
- 3. Efficient computation3.1. Tree data structure with leaf links; 3.2. Leaf traversal algorithm; 4. Selecting feature groups and weights; 4.1. Reduction of spectrum string kernel; 4.2. Statistically selecting feature groups; 5. Experiment; 6. Discussion and future work; Acknowledgments; References; Predicting Nucleolar Proteins Using Support-Vector Machines Mikael Bod 1. Introduction; 2. Background; 3. Methods; 3.1. Data set; 3.2. Model; 4. Results; 5 . Conclusion; Acknowledgments; References.
- Supervised Ensembles of Prediction Methods for Subcellular Localization Johannes Apfalg, Jing Gong, Hans-Peter Kriegel, Alexey Pryakhin, Tiandi Wei and Arthur Zimek1. Introduction; 2. Survey on Prominent Prediction Methods for Subcellular Localization; 2.1. Amino Acid Composition; 2.2. Sorting Signals; 2.3. Homology; 2.4. Hybrid Methods; 3. Ensemble Methods; 3.1. Theory; 3.2. Selection of Base Methods for Ensembles; 3.3. Ensemble Method Based on a Voting Schema; 3.4. Ensemble Method Based on Decision Trees; 4. Evaluation; 5. Conclusions; References.
- Chemical Compound Classification with Automatically Mined Structure Patterns Aaron M. Smalter, J. Huan and Gerald H. Lushington1. Introduction; 2. Related Work; 2.1. Marginalized and Optimal Assignment Graph Kernels; 2.2. Frequent Subgraph Mining; 3. Background; 3.1. Chemical Structure; 4. Algorithm Design; 4.1. Structure Pattern Mining; 4.2. Optimal Assignment Kernel; 4.3. Reduced Graph Representation; 4.4. Pattern-based Descriptors; 5. Experimental Study; 5.1. Data Sets; 5.2. Methods; 5.3. Results; 6. Conclusions; Acknowledgments; References.
- Structure-Approximating Design of Stable Proteins in 2D HP Model Fortified by Cysteine Monomers Alireza Hadj Khodabakhshi, Jdn Mariuch, Arash Rafiey and Arvind Gupta1. Introduction; 2. Definitions; 2.1. Hydropho bic-polar- c ysteine (HP C) model; 2.2. Snake structures; 2.3. The strong HPC model; 3. Proof techniques; 3.1. Saturated folds; 3.2. 2DHPSolver: a semi-automatic prover; 4. Stability of the snake structures; 5. Conclusions; References; Discrimination of Native Folds Using Network Properties of Protein Structures Alper Kiiciikural, 0. Ug'ur Sezerman and Aytiil Ercal; 1 Introduction.