Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures
<p>Abstract</p> <p>Background</p> <p>DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2008-06-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/268 |
| _version_ | 1818917530577666048 |
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| author | Broach James R Smith Erin N Tan Meng P Floudas Christodoulos A |
| author_facet | Broach James R Smith Erin N Tan Meng P Floudas Christodoulos A |
| author_sort | Broach James R |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the rapid and comprehensive interpretation of this data is gene clustering with respect to the expression patterns. Classifying genes into clusters can lead to interesting biological insights. In this study, we describe an iterative clustering approach to uncover biologically coherent structures from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust.</p> <p>Results</p> <p>We apply our proposed iterative algorithm to three sets of experimental DNA microarray data from experiments with the yeast <it>Saccharomyces cerevisiae </it>and show that the proposed iterative approach improves biological coherence. Comparison with other clustering techniques suggests that our iterative algorithm provides superior performance with regard to biological coherence. An important consequence of our approach is that an increasing proportion of genes find membership in clusters of high biological coherence and that the average cluster specificity improves.</p> <p>Conclusion</p> <p>The results from these clustering experiments provide a robust basis for extracting motifs and trans-acting factors that determine particular patterns of expression. In addition, the biological coherence of the clusters is iteratively assessed independently of the clustering. Thus, this method will not be severely impacted by functional annotations that are missing, inaccurate, or sparse.</p> |
| first_indexed | 2024-12-20T00:35:32Z |
| format | Article |
| id | doaj.art-fa24d1e0aa1947c49e0d01ffcef63c6c |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-20T00:35:32Z |
| publishDate | 2008-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-fa24d1e0aa1947c49e0d01ffcef63c6c2022-12-21T19:59:46ZengBMCBMC Bioinformatics1471-21052008-06-019126810.1186/1471-2105-9-268Microarray data mining: A novel optimization-based approach to uncover biologically coherent structuresBroach James RSmith Erin NTan Meng PFloudas Christodoulos A<p>Abstract</p> <p>Background</p> <p>DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the rapid and comprehensive interpretation of this data is gene clustering with respect to the expression patterns. Classifying genes into clusters can lead to interesting biological insights. In this study, we describe an iterative clustering approach to uncover biologically coherent structures from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust.</p> <p>Results</p> <p>We apply our proposed iterative algorithm to three sets of experimental DNA microarray data from experiments with the yeast <it>Saccharomyces cerevisiae </it>and show that the proposed iterative approach improves biological coherence. Comparison with other clustering techniques suggests that our iterative algorithm provides superior performance with regard to biological coherence. An important consequence of our approach is that an increasing proportion of genes find membership in clusters of high biological coherence and that the average cluster specificity improves.</p> <p>Conclusion</p> <p>The results from these clustering experiments provide a robust basis for extracting motifs and trans-acting factors that determine particular patterns of expression. In addition, the biological coherence of the clusters is iteratively assessed independently of the clustering. Thus, this method will not be severely impacted by functional annotations that are missing, inaccurate, or sparse.</p>http://www.biomedcentral.com/1471-2105/9/268 |
| spellingShingle | Broach James R Smith Erin N Tan Meng P Floudas Christodoulos A Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures BMC Bioinformatics |
| title | Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures |
| title_full | Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures |
| title_fullStr | Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures |
| title_full_unstemmed | Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures |
| title_short | Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures |
| title_sort | microarray data mining a novel optimization based approach to uncover biologically coherent structures |
| url | http://www.biomedcentral.com/1471-2105/9/268 |
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