Big Data Analytics in Genomics [E-Book] / edited by Ka-Chun Wong.
This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at a...
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Full text |
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Personal Name(s): | Wong, Ka-Chun, editor |
Imprint: |
Cham :
Springer International Publishing,
2016
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Physical Description: |
VIII, 428 p. 70 illus., 58 illus. in color. online resource. |
Note: |
englisch |
ISBN: |
9783319412795 |
DOI: |
10.1007/978-3-319-41279-5 |
Subject (ZB): | |
Subject (LOC): | |
Classification: |
- 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.