Abstract
For a microarray dataset with attached phenotype information – which gives expression levels of various genes and a phenotype classification for each of a set of samples – an important problem is to find informative genes. These genes have high information content as attributes for classification, minimizing the expected number of tests needed to identify a phenotype. This study investigates the use of a heuristic method for finding complete sets of informative genes (sets that are sufficient for constructing a maximally discriminating classifier) that are as small as possible. These minimal sets of informative genes can be very useful in developing an appreciation for the data. Our method uses branch-and-bound depth-first search. Experimental results suggest that our method is effective in finding minimal gene sets, and the resulting classifiers have good performance in terms of classification accuracy.
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© 2007 Springer-Verlag Berlin Heidelberg
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Chang, KH., Kwon, Y.K., Parker, D.S. (2007). Finding Minimal Sets of Informative Genes in Microarray Data. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_21
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DOI: https://doi.org/10.1007/978-3-540-72031-7_21
Publisher Name: Springer, Berlin, Heidelberg
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