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

Neolithic

NGS Analysis of Somatic Mutations in Cancer Genomese analysis of these data has confirmed the early predictions of extensive sequence and structural diversity of cancer genomes, fueling the development of new computational approaches to decipher inter- and intratumoral somatic variation within and among cancer patients. Overall, these techniques hav

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OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancerhich scientists can explore the overall mutational landscape in patients with various types of cancers. We have developed the OncoMiner pipeline for mining WES data to identify exonic sequence variants, link them with associated research literature, visualize their genomic locations, and compare the

CHOKE

A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancerreatments. The serious problem is that the patients, called “triple negative” (TN), who cannot be fallen into any of these three categories, have no clear treatment options. Thus linking TN patients to the main three phenotypes clinically is very important. Usually BC patients are profiled by gene e

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盡忠

,Vers un système de gestion de données,arch interest. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A?major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to b

Lipohypertrophy

https://doi.org/10.1007/2-287-31090-8measurements. Causal networks have been widely used in systems genetics for modeling gene regulatory systems and for identifying causes and risk factors of diseases. In this chapter, we describe fundamental concepts and algorithms for constructing causal networks from observational data. In biologic

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https://doi.org/10.1007/2-287-31090-8controlling 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

modifier

https://doi.org/10.1007/2-287-31090-8e 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

自作多情

https://doi.org/10.1007/2-287-31090-8 accurate method for this kind of search. Unfortunately, this algorithm is computationally demanding and the situation gets worse due to the exponential growth of biological data in the last years. For that reason, the scientific community has made great efforts to accelerate Smith–Waterman biologic

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