Evolutionary Approach for Relative Gene Expression Algorithms
A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple res...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/593503 |
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| _version_ | 1832549079417094144 |
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| author | Marcin Czajkowski Marek Kretowski |
| author_facet | Marcin Czajkowski Marek Kretowski |
| author_sort | Marcin Czajkowski |
| collection | DOAJ |
| description | A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space. |
| format | Article |
| id | doaj-art-1e19ddc459034dcd95d93313f7e8fa31 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-1e19ddc459034dcd95d93313f7e8fa312025-02-03T06:12:17ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/593503593503Evolutionary Approach for Relative Gene Expression AlgorithmsMarcin Czajkowski0Marek Kretowski1Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, PolandFaculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, PolandA Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space.http://dx.doi.org/10.1155/2014/593503 |
| spellingShingle | Marcin Czajkowski Marek Kretowski Evolutionary Approach for Relative Gene Expression Algorithms The Scientific World Journal |
| title | Evolutionary Approach for Relative Gene Expression Algorithms |
| title_full | Evolutionary Approach for Relative Gene Expression Algorithms |
| title_fullStr | Evolutionary Approach for Relative Gene Expression Algorithms |
| title_full_unstemmed | Evolutionary Approach for Relative Gene Expression Algorithms |
| title_short | Evolutionary Approach for Relative Gene Expression Algorithms |
| title_sort | evolutionary approach for relative gene expression algorithms |
| url | http://dx.doi.org/10.1155/2014/593503 |
| work_keys_str_mv | AT marcinczajkowski evolutionaryapproachforrelativegeneexpressionalgorithms AT marekkretowski evolutionaryapproachforrelativegeneexpressionalgorithms |