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Comparison of clustering approaches for gene expression data
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.ORCID-id: 0000-0002-8929-7220
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
2013 (engelsk)Inngår i: Frontiers in Artificial Intelligence and Applications, IOS Press , 2013, Vol. 257, s. 55-64Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Clustering algorithms have been used to divide genes into groups according to the degree of their expression similarity. Such a grouping may suggest that the respective genes are correlated and/or co-regulated, and subsequently indicates that the genes could possibly share a common biological role. In this paper, four clustering algorithms are investigated: k-means, cut-clustering, spectral and expectation-maximization. The algorithms are benchmarked against each other. The performance of the four clustering algorithms is studied on time series expression data using Dynamic TimeWarping distance in order to measure similarity between gene expression profiles. Four different cluster validation measures are used to evaluate the clustering algorithms: Connectivity and Silhouette Index for estimating the quality of clusters, Jaccard Index for evaluating the stability of a cluster method and Rand Index for assessing the accuracy. The obtained results are analyzed by Friedman's test and the Nemenyi post-hoc test. K-means is demonstrated to be significantly better than the spectral clustering algorithm under the Silhouette and Rand validation indices.

sted, utgiver, år, opplag, sider
IOS Press , 2013. Vol. 257, s. 55-64
Emneord [en]
Dynamic time warping, Gene expression data, Graph-based clustering algorithm, Minimum cut clustering, Partitioning algorithm
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-6357DOI: 10.3233/978-1-61499-330-8-55ISI: 000343477100007Lokal ID: oai:bth.se:forskinfoF6F6EAEE6B2430D2C1257CA600374AA9OAI: oai:DiVA.org:bth-6357DiVA, id: diva2:833855
Konferanse
Scandinavian Conference on Artificial Intelligence SCAI 2013
Tilgjengelig fra: 2015-05-25 Laget: 2014-03-25 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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Totalt: 228 treff
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