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

By Darius M. Dziuda

Facts Mining for Genomics and Proteomics makes use of pragmatic examples and an entire case learn to illustrate step by step how biomedical experiences can be utilized to maximise the opportunity of extracting new and precious biomedical wisdom from facts. it really is an exceptional source for college kids and execs concerned with gene or protein expression information in a number of settings.

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

Sample text

The MAS5 algorithm is included in the Affymetrix Expression Console software (Affymetrix 2006). MAS5 Background Adjustment Adjustment for Background To estimate the background intensity, MAS5 divides each array into K equally sized rectangular zones, with K ¼ 16 as the default number of zones. For each zone k, k ¼ 1, . . , K, the mean and the standard deviation of the lowest two percent of the probe intensities in the zone are used to estimate the zone’s mean background bk and noise nk (the background variability).

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). Then, the splicing process removes introns, joins exons, and creates mRNA (called also mature mRNA). The mRNA travels from the nucleus to the cytoplasm where, in ribosomes, it is translated into a protein.

Different data formats and often insufficient annotations and descriptions of experiments made it rather difficult to verify and compare the published studies. Furthermore, finding independent (also large enough and of good quality) test data sets was practically impossible without direct collaboration with other groups generating such data for the same area of biomedical research. 23 Therefore, sufficient information about the data and their processing is crucial for the interpretation and comparison of results and for the integration of data from different experiments (Brazma et al.

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