Abstract
In light of the fast growth in DNA technology there is a compelling demand for tools able to perform efficient, exhaustive and integrative analyses of multiple microarray datasets. Specifically, what is particularly evident is the need to link the results obtained from these new tools with the wealth of clinical information. The final goal is to bridge the gap existing between biomedical researchers and pathologists or oncologists providing them with a common framework of interaction. To overcome such difficulty we have developed geneCBR, a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction. In diagnostic mode, geneCBR employs a case-based reasoning model that incorporates a set of fuzzy prototypes for the retrieval of relevant genes, a growing cell structure network for the clustering of similar patients and a proportional weighted voting algorithm to provide an accurate diagnosis.
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Glez-Peña, D., Glez-Bedia, M., Díaz, F., Fdez-Riverola, F. (2009). Multiple-Microarray Analysis and Internet Gathering Information with Application for Aiding Medical Diagnosis in Cancer Research. In: Corchado, J.M., De Paz, J.F., Rocha, M.P., Fernández Riverola, F. (eds) 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85861-4_14
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DOI: https://doi.org/10.1007/978-3-540-85861-4_14
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