NDLI: Mining Order Preserving Patterns in Microarray Data by Finding Frequent Orders

Content Provider	IEEE Xplore Digital Library
Author	Li Teng Laiwan Chan
Copyright Year	2007
Description	Author affiliation: Chinese Univ. of Hong Kong, Hong Kong (Li Teng; Laiwan Chan)
Abstract	This paper concerns the discovery of order preserving clusters (OP-clusters) in microarray data, in each of which a subset of genes induce a similar linear ordering along a subset of conditions. By converting each gene expression vector into an ordered label sequence, we transfer the problem into finding frequent orders appearing in the sequence set. We present two heuristic algorithms growing prefix and suffix (GPS) and growing frequent position (GFP), to solve this problem. Their performance is evaluated empirically using synthetic and real microarray data. The result shows our approaches are effective and efficient and outperform existing methods in many aspects. The two proposed algorithms, GPS and GFP, both have good scale-up properties with the dimension of the dataset and the size of the clusters. They have comparable performance, albeit GPS gets higher precision, whereas GFP has lower computation cost.
Starting Page	1019
Ending Page	1026
File Size	867586
Page Count	8
File Format	PDF
ISBN	9781424415090
DOI	10.1109/BIBE.2007.4375682
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Global Positioning System Gene expression Clustering algorithms Vectors Heuristic algorithms Computational efficiency Data analysis Computer science Data engineering High performance computing
Content Type	Text
Resource Type	Article

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