NDLI: Associative Artificial Neural Network for Discovery of Highly Correlated Gene Groups Based on Gene Ontology and Gene Expression

Content Provider	IEEE Xplore Digital Library
Author	Ji He Xinbin Dai Xuechun Zhao
Copyright Year	2007
Description	Author affiliation: Plant Biol. Div., Samuel Roberts Noble Found., Ardmore, OK (Ji He; Xinbin Dai; Xuechun Zhao)
Abstract	The advance of high-throughput experimental technologies poses continuous challenges to computational data analysis in functional and comparative genomics studies. Gene ontology (GO) annotation and transcriptional profiling using gene expression array have been two of the major approaches for system-wide analysis of gene functions and gene interactions. In the literature, extensive studies have been reported in each aspect. Yet there is a lack of efficient algorithm that discover associative patterns across these two data domains. We proposed a mixture model associative artificial neural network to tackle this deficiency. The algorithm inherits the theoretical foundation of adaptive resonance associative map (ARAM), with essential redefinition of pattern similarity measures and learning functions. The proposed algorithm is capable of clustering data based on both GO semantic similarity and expressional correlation, for the purpose of systematically discovering genome-wide, highly correlated gene groups, which in turn suggest similar or closely related functions. We applied the proposed algorithm to the analysis of the Saccharomyces cerevisiae (yeast) dataset and obtained satisfactory results.
Starting Page	17
Ending Page	24
File Size	322393
Page Count	8
File Format	PDF
ISBN	1424407109
DOI	10.1109/CIBCB.2007.4221199
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Ontologies Gene expression Clustering algorithms Genomics Bioinformatics Data analysis Resonance Algorithm design and analysis Fungi
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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