NDLI: A comparative study of genetic sequence classification algorithms

Content Provider	IEEE Xplore Digital Library
Author	Mukhopadhyay, S. Changhong Tang Huang, J. Mulong Yu Palakal, M.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Indiana Univ., Indianapolis, IN, USA (Mukhopadhyay, S.; Changhong Tang; Huang, J.; Mulong Yu; Palakal, M.)
Abstract	Classification of genetic sequence data available in public and private databases is an important problem in using, understanding, retrieving, filtering and correlating such large volumes of information. Although a significant amount of research effort is being spent internationally on this problem, very few studies exist that compare different classification approaches in terms of an objective and quantitative classification performance criterion. In this paper, we present experimental studies for classification of genetic sequences using both unsupervised and supervised approaches, focusing on both computational effort as well as a suitably defined classification performance measure. The results indicate that both unsupervised classification using the Maximin algorithm combined with FASTA sequence alignment algorithm and supervised classification using artificial neural network have good classification performance, with the unsupervised classification performs better and the supervised classification performs faster. A trade-off between the quality of classification and the computational efforts exists. The utilization of these classifiers for retrieval, filtering and correlation of genetic information as well as prediction of functions and structures will be logical future directions for further research.
Sponsorship	IEEE Signal Process. Soc. IEEE Neural Network Council Swiss Nat. Sci. Found
Starting Page	57
Ending Page	66
File Size	412340
Page Count	10
File Format	PDF
ISBN	0780376161
DOI	10.1109/NNSP.2002.1030017
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-06
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetics Classification algorithms Sequences Databases Clustering algorithms Artificial neural networks Information retrieval Information filtering Information filters Frequency
Content Type	Text
Resource Type	Article

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