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Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge
Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel mult...
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Published in: | Cancer medicine (Malden, MA) MA), 2015-12, Vol.4 (12), p.1884-1900 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization‐based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.
This work presents a method to analyze simultaneously multiple microarray experiments. The method does not require for the user to adjust parameters of any sort to detect highly differentially expressed genes. The method is applied to the analysis of lung cancer. |
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ISSN: | 2045-7634 2045-7634 |
DOI: | 10.1002/cam4.540 |