Download Data Mining for Genomics and Proteomics: Analysis of Gene by Darius M. Dziuda PDF

By Darius M. Dziuda

Info Mining for Genomics and Proteomics makes use of pragmatic examples and a whole case research to illustrate step by step how biomedical reports can be utilized to maximise the opportunity of extracting new and important biomedical wisdom from information. it truly is a great source for college students and pros concerned with gene or protein expression facts in numerous settings.

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Extra info for Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data (Wiley Series on Methods and Applications in Data Mining)

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On the other hand, many diseases are not necessarily related to genetic abnormalities, but are caused by the environmental factors that are changing the expression level of otherwise “normal” genes. This may be more complicated when such changes in the gene expression level are still moderated by some gene mutations, which by themselves are not causing any diseases. And here is the promise of genomics—identification of gene expression patterns associated with a disease and linking the patterns to underlying biological and environmental factors may lead to cure or prevention.

The transcribed region consists of exons that are interrupted by introns (on average, introns are about 20 times longer than exons). The promoter associated with the gene is a regulatory sequence that facilitates initiation of gene transcription and controls gene expression. Enhancers and silencers are other gene-associated regulatory sequences that may activate or repress transcription of the gene (they are not shown here as they are often located distantly from the transcribed region). The gene’s exons and introns are first transcribed into a complementary RNA (called nuclear RNA, primary RNA transcript or pre-mRNA).

One could say that higher-level preprocessing, exploratory data analysis, and mining of gene expression data start after we have the gene expression levels available in the form of a single number representing the abundance of a gene transcript in a biological sample. However, knowing where data come from and what methods were applied to convert the raw data into this “starting point” expression data is very important for assessing the quality of the data, validity of experimental design and selection of particular approaches.

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