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Big Data Analytics in Genomics / / edited by Ka-Chun Wong
| Big Data Analytics in Genomics / / edited by Ka-Chun Wong |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (VIII, 428 p. 70 illus., 58 illus. in color.) |
| Disciplina | 570.285 |
| Soggetto topico |
Bioinformatics
Data mining Statistics Biomathematics Computational Biology/Bioinformatics Data Mining and Knowledge Discovery Statistics for Life Sciences, Medicine, Health Sciences Genetics and Population Dynamics |
| ISBN | 9783319412795 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Statistical Methods for Integrative Analysis of Genomic Data -- Robust Methods for Expression Quantitative Trait Loci Mapping -- Causal Inference and Structure Learning of Genotype-Phenotype Networks using Genetic Variation -- Genomic Applications of the Neyman-Pearson Classification Paradigm -- Improving Re-annotation of Annotated Eukaryotic Genomes -- State-of-the-art in Smith-Waterman Protein Database Search -- A Survey of Computational Methods for Protein Function Prediction -- Genome Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast -- Perspectives of Machine Learning Techniques in Big Data Mining of Cancer -- Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms -- NGC Analysis of Somatic Mutations in Cancer Genomes -- OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer -- A Bioinformatics Approach for Understanding Genotype-Phenotype Correlation in Breast Cancer. |
| Record Nr. | UNINA-9910148856803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Natural Computing for Unsupervised Learning / / edited by Xiangtao Li, Ka-Chun Wong
| Natural Computing for Unsupervised Learning / / edited by Xiangtao Li, Ka-Chun Wong |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (272 pages) : illustrations |
| Disciplina | 006.38 |
| Collana | Unsupervised and Semi-Supervised Learning |
| Soggetto topico |
Electrical engineering
Signal processing Image processing Speech processing systems Pattern recognition Artificial intelligence Data mining Communications Engineering, Networks Signal, Image and Speech Processing Pattern Recognition Artificial Intelligence Data Mining and Knowledge Discovery |
| ISBN | 3-319-98566-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part I – Basic Natural Computing Techniques for Unsupervised Learning -- Hard Clustering using Evolutionary Algorithms -- Soft Clustering using Evolutionary Algorithms -- Fuzzy / Rough Set Systems for Unsupervised Learning -- Unsupervised Feature Selection using Evolutionary Algorithms -- Unsupervised Feature Selection using Artificial Neural Networks -- Part II – Advanced Natural Computing Techniques for Unsupervised Learning -- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering -- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection -- Co-Evolutionary Approaches for Unsupervised Learning -- Mining Evolving Patterns using Natural Computing Techniques -- Multi-objective Optimization for Unsupervised Learning -- Many-objective Optimization for Unsupervised Learning -- Part III – Applications -- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques -- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data -- Natural Computing Techniques for Community Detection on Online Social Networks -- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning -- Conclusion. |
| Record Nr. | UNINA-9910337471503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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