NDLI: Biclustering of DNA microarray data using artificial immune system

Content Provider	IEEE Xplore Digital Library
Author	Abohamad, W. Korayem, M. Moustafa, K.
Copyright Year	2010
Description	Author affiliation: Faculty of Computers and Information, Cairo University, Egypt (Abohamad, W.; Moustafa, K.) \|\| Faculty Of Computers and Information, Fayoum University, Egypt (Korayem, M.)
Abstract	Advances in DNA microarray technology has motivated the research community to introduce sophisticated techniques for analyzing the resulted large-scale datasets. Biclustering techniques have been widely adapted for analyzing microarray gene expression data due to its ability to extract local patterns with a subset of genes that are similarly expressed over a subset of samples. Mostly, biclustering methods are based on greedy heuristics which often result in suboptimal solutions. To this end, this paper presents a clonal selection algorithm for biclustering (Bic-CSA) that incorporates these greedy searching procedures as local search heuristics in an immune-inspired algorithm. The quality of biclusters has been demonstrated by experimentation on a well known benchmark dataset. Moreover, the performance of Bic-CSA is compared with other related local search-based methods and immune inspired algorithms. It is shown from results and comparative study that the proposed algorithm outperforms other algorithms in terms of bicluster size and mean-squared residue.
Starting Page	1223
Ending Page	1228
File Size	160228
Page Count	6
File Format	PDF
ISBN	9781424481347
e-ISBN	9781424481361
DOI	10.1109/ISDA.2010.5687017
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-29
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Biclustering Heuristic algorithms Cloning DNA Gene Expression Data Artificial Immune System Gene expression Gene Ontology DNA Microarray Immune system Clonal Selection Algorithm
Content Type	Text
Resource Type	Article

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