Abstract
Scatter Search is an evolutionary method that combines existing solutions to create new offspring as the well–known genetic algorithms. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. However, biclusters with certain patterns are more interesting from a biological point of view. Therefore, the proposed Scatter Search uses a measure based on linear correlations among genes to evaluate the quality of biclusters. As it is usual in Scatter Search methodology an improvement method is included which avoids to find biclusters with negatively correlated genes. Experimental results from yeast cell cycle and human B-cell lymphoma datasets are reported showing a remarkable performance of the proposed method and measure.
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Nepomuceno, J.A., Troncoso, A., Aguilar–Ruiz, J.S. (2010). Correlation–Based Scatter Search for Discovering Biclusters from Gene Expression Data. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_11
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DOI: https://doi.org/10.1007/978-3-642-12211-8_11
Publisher Name: Springer, Berlin, Heidelberg
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