Titlebook: Clinical Bioinformatics; Ronald J.A. Trent Book 2008 Humana Press 2008 DNA.In silico.Microarray.Proteomics.SNP.bioinformatics.gene express

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https://doi.org/10.1007/978-1-60327-148-6DNA; In silico; Microarray; Proteomics; SNP; bioinformatics; gene expression; genes; genome; sequence analysi

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L. J. Liu,H. M. Gao,G. J. Zhang,L. Wu can significantly improve their detection. Data mining and automated tracking of new knowledge facilitate locus mapping. At the gene search stage, . prioritization of candidate genes plays an indispensable role in dealing with linked or associated loci. . analysis can also differentiate subtle cons

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Yoshiyuki Kojima,Pasquale Casaleg these diseases. However, the polygenic nature of the complex diseases and genetic interactions among the genes pose significant challenge in both experimental design and data analysis. High-density genotype data make it possible to identify most of the genetic loci that may be involved in the etio

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Sarah P. Conley,Benjamin R. Leele to study most of the human genetic diversity that is due to common variations in relation to observable phenotypes. Over the past few years, public SNP databases have matured and empirical genome-wide SNP data, such as that generated by the International HapMap Project, have shown the utility and

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Shahin Chandrasoma,Chester J. Kohre unknown. It is possible to estimate the relative importance of the influence of genes and environment on a trait by studying correlations in the trait in related individuals. Known risk factors can be measured and included in the statistical models to understand disease etiology better. The joint

MONY

https://doi.org/10.1007/978-1-60327-914-7his technology. Investigators often require answers to questions relating to microarray platforms, RNA samples, options for replication, allocation of samples to arrays, sample sizes, appropriate downstream analysis, and many others. Careful consideration of these issues is critical to ensure the ef

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Yoshiyuki Kojima,Pasquale Casalemicroarray technology to identify genes that are differentially regulated in response to activin-treated ovarian cancer cells. We find a number of biologically relevant genes that are involved in regulating activin signaling and genes potentially contributing to activin-mediated growth arrest appear