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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 |
MARC
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| 300 | |a 1 online resource (413 pages) | ||
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| 588 | 0 | |a Print version record. | |
| 520 | |a 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 containing large numbers of sequences, polymorphisms, structures, metabolic pathways, and gene expression profiles of normal and diseased tissues are rapidly being generated for human and model organisms. Bioinformatics is therefore gaining importance in the annotation of genomic sequences; the understand. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Bioinformatics |v Congresses. | |
| 650 | 0 | |a Biology |x Data processing |v Congresses. | |
| 650 | 0 | |a Biology. | |
| 650 | 0 | |a Life sciences. | |
| 650 | 0 | |a Physical sciences. | |
| 650 | 2 | |a Biology | |
| 650 | 2 | |a Biological Science Disciplines | |
| 650 | 2 | |a Natural Science Disciplines | |
| 650 | 2 | |a Disciplines and Occupations | |
| 650 | 2 | |a Computational Biology | |
| 650 | 6 | |a Bio-informatique |v Congrès. | |
| 650 | 6 | |a Biologie |x Informatique |v Congrès. | |
| 650 | 6 | |a Biologie. | |
| 650 | 6 | |a Sciences de la vie. | |
| 650 | 6 | |a Sciences physiques. | |
| 650 | 6 | |a Bio-informatique. | |
| 650 | 7 | |a biology. |2 aat | |
| 650 | 7 | |a biological sciences. |2 aat | |
| 650 | 7 | |a physical sciences. |2 aat | |
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| 650 | 7 | |a Life sciences |2 fast | |
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| 650 | 7 | |a Bioinformatics |2 fast | |
| 650 | 7 | |a Biology |x Data processing |2 fast | |
| 655 | 7 | |a Conference papers and proceedings |2 fast | |
| 700 | 1 | |a Miyano, Satoru. | |
| 700 | 1 | |a Akutsu, Tatsuya. | |
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| 776 | 0 | 8 | |i Print version: |z 9781848161085 |
| 830 | 0 | |a Series on advances in bioinformatics and computational biology. | |
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