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

By Darius M. Dziuda

Info Mining for Genomics and Proteomics makes use of pragmatic examples and a whole case learn to illustrate step by step how biomedical reports can be utilized to maximise the opportunity of extracting new and important biomedical wisdom from facts. it's a good source for college students and execs concerned with gene or protein expression information in quite a few settings.

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

Example text

5 Data Mining We define data mining as efficient ways of extracting new information or new knowledge from large data sets or databases. ’ However, in biomedical research any new information reflecting underlying biological processes can be potentially useful. Furthermore, translating the extracted new information into new biomedical knowledge is often a nontrivial task and the data mining definition (and methods) should be extended as well to include the information-to-knowledge stage of investigations.

We want to stress the importance of selecting the right methods for the task. This should be obvious, but it is not necessarily the case for some biomedical studies reported in the literature. All too often, the data at hand is analyzed with a tool that happens to be available and popular, whether or not it is the proper tool to achieve the goal of a study. This, unfortunately, usually leads to under-usage of the data and the reporting of inferior results in situations where the information or knowledge sought could have been extracted from the data had appropriate methods and tools been used.

The MAS5 method is the oldest among these and seems to have gradually being phased out by PLIER (Affymetrix 2005b)—a newer method from Affymetrix. 3 summarizes the main characteristics of these methods which are discussed in the following sections. Some data miners (or bioinformaticians) have a tendency to treat initial preprocessing as a “black box,” and assume that the real analysis starts when the gene expression level for each gene has already been calculated. Although one could understand this approach, in real research projects it is important to know how the gene expressions were calculated.

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