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Diagnostic Imaging

Bioarrays: From Basics to Diagnostics by Krishnarao Appasani, Sir E.M. Southern

By Krishnarao Appasani, Sir E.M. Southern

This ebook presents an built-in number of well timed articles at the use of bioarray recommendations within the fields of biotechnology and molecular medication. it's the first publication to comprehensively combine molecular diagnostics and molecular pathology. This publication serves as an necessary reference for graduate scholars, post-docs, and professors in addition to an explanatory research for executives and scientists in biotechnology and pharmaceutical businesses.

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Background How are complex organisms such as humans formed from a single cell? How are tissues differentiated? How do cells function in different environments? What changes occur in diseases? Such questions can be assessed using good biological models in combination with a comprehensive assessment of gene expression patterns. Whether you want to view the entire genome on a single array or focus on a target set of biologically relevant genes, microarrays allow the analysis of gene expression levels and can be applied in a broad spectrum of questions, including uncovering new regulatory pathways, validating drug targets, clarifying diseases, analyzing toxicological responses, or building robust databases (1–9).

3. Validation of Candidate Genes Through Real-Time Reverse Transcription-PCR and Tissue Microarray Cancer research has benefited from several advances in technology, and this advance has resulted in an exponential increase in the number of novel gene candidates to be implicated in tumorigenesis, diagnosis, prognosis, and new therapeutic targets. The large amount of new information has created a new challenge in the identification of an individual gene, and mainly in its validation as a clinically useful diagnostic or prognostic marker.

L. (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed. Opt. 2, 364–374. 37. Butte, A. (2002) The use and analysis of microarray data. Nat. Rev. Drug Discov. 1, 951–960. 38. Yang, I. , Hasseman, J. , et al. (2002) Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol. 13. 39. , and Churchill, G. A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol.

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