Abstract
Many heuristics used for finding biclusters in microarray data use the mean squared residue as a way of evaluating the quality of biclusters. This has led to the discovery of interesting biclusters. Recently it has been proven that the mean squared residue may fail to identify some interesting biclusters. This motivates us to introduce a new measure, called Virtual Error, for assessing the quality of biclusters in microarray data. In order to test the validity of the proposed measure, we include it within an evolutionary algorithm. Experimental results show that the use of this novel measure is effective for finding interesting biclusters, which could not have been discovered with the use of the mean squared residue.
Keywords
- Microarray Data
- Evolutionary Algorithm
- Gene Expression Data
- Expression Matrix
- Scaling Pattern
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Pontes, B., Divina, F., Giráldez, R., Aguilar–Ruiz, J.S. (2007). Virtual Error: A New Measure for Evolutionary Biclustering. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_21
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DOI: https://doi.org/10.1007/978-3-540-71783-6_21
Publisher Name: Springer, Berlin, Heidelberg
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