Gene microarray data analysis using parallel point-symmetry-based clustering
Sarkar, Anasua LU
and Maulik, Ujjwal
(2015)
In International Journal of Data Mining and Bioinformatics
11(3).
p.277-300
- Abstract
Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or nonconvex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetrybased K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based... (More)
Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or nonconvex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetrybased K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based K-Means algorithm is compared with another new parallel symmetry-based K-Means and existing parallel K-Means over eight artificial and benchmark microarray data sets, to demonstrate its superiority, in both timing and validity. The statistical analysis is also performed to establish the significance of this message-passing-interface based point-symmetry K-Means implementation. We also analysed the biological relevance of clustering solutions.
(Less)
- author
- Sarkar, Anasua
LU
and Maulik, Ujjwal
- publishing date
- 2015-01-01
- type
- Contribution to journal
- publication status
- published
- keywords
- Bioinformatics, Cluster validity measures, Clustering algorithm, K-Means algorithm, Microarray gene expression, Parallel algorithm, Point-symmetry based distance
- in
- International Journal of Data Mining and Bioinformatics
- volume
- 11
- issue
- 3
- pages
- 24 pages
- publisher
- Inderscience Publishers
- external identifiers
-
- pmid:26333263
- scopus:84922583793
- ISSN
- 1748-5673
- DOI
- 10.1504/IJDMB.2015.067320
- language
- English
- LU publication?
- no
- id
- fc1b698d-0372-4001-a5e4-3ee6702c3a6c
- date added to LUP
- 2018-10-09 09:46:08
- date last changed
- 2024-02-14 04:24:17
@article{fc1b698d-0372-4001-a5e4-3ee6702c3a6c,
abstract = {{<p>Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or nonconvex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetrybased K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based K-Means algorithm is compared with another new parallel symmetry-based K-Means and existing parallel K-Means over eight artificial and benchmark microarray data sets, to demonstrate its superiority, in both timing and validity. The statistical analysis is also performed to establish the significance of this message-passing-interface based point-symmetry K-Means implementation. We also analysed the biological relevance of clustering solutions.</p>}},
author = {{Sarkar, Anasua and Maulik, Ujjwal}},
issn = {{1748-5673}},
keywords = {{Bioinformatics; Cluster validity measures; Clustering algorithm; K-Means algorithm; Microarray gene expression; Parallel algorithm; Point-symmetry based distance}},
language = {{eng}},
month = {{01}},
number = {{3}},
pages = {{277--300}},
publisher = {{Inderscience Publishers}},
series = {{International Journal of Data Mining and Bioinformatics}},
title = {{Gene microarray data analysis using parallel point-symmetry-based clustering}},
url = {{http://dx.doi.org/10.1504/IJDMB.2015.067320}},
doi = {{10.1504/IJDMB.2015.067320}},
volume = {{11}},
year = {{2015}},
}