Titlebook: Big Data Analytics in Genomics; Ka-Chun Wong Book 2016 Springer International Publishing Switzerland (Outside the USA) 2016 Big Data.Genom

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Genomic Applications of the Neyman–Pearson Classification Paradigmcontrolling type I error under some specified level ., usually a small number. This problem is often faced in many genomic applications involving binary classification tasks. The terminology Neyman–Pearson classification paradigm arises from its connection to the Neyman–Pearson paradigm in hypothesi

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Improving Re-annotation of Annotated Eukaryotic Genomese annotated sequenced genome of the corresponding organism and improve the existing gene models. In addition, misleading annotations propagate in multiple databases by comparative approaches of annotation, automatic annotation, and lack of curating power in the face of large data volume. In this pur

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Genome-Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeasthanges. Its positioning across the genome leaves a significant impact on the DNA dependent processes, particularly on gene regulation. Though they form structural repeating units of chromatin they differ from each other by DNA/histone covalent modifications establishing diversity in natural populati

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Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Alg entire genome at the DNA, RNA, protein, and epigenetic levels. Due to the complex nature of cancer, several organizations have launched comprehensive molecular profiling for thousands of cancer patients using multiple high-throughput technologies to investigate cancer genomics, transcriptomics, pro