NDLI: Classification method for microarray probe selection using sequence, thermodynamics and secondary structure parameters

Content Provider	IEEE Xplore Digital Library
Author	Gupta, L. Kumar, S. Singh, R. Shaik, R. Dimitrova, N. Gorthi, A. Lakshmi, B. Pai, D. Kamalakaran, S. Zhao, X. Wigler, M.
Copyright Year	2008
Description	Author affiliation: Philips Res. Asia - Bangalore, Bangalore (Gupta, L.; Kumar, S.; Singh, R.; Shaik, R.; Dimitrova, N.; Gorthi, A.; Kamalakaran, S.) \|\| Cold Spring Harbor Lab., Spring, NY (Lakshmi, B.; Pai, D.; Zhao, X.; Wigler, M.)
Abstract	Probe design is the most important step for any microarray based assay. Accurate and efficient probe design and selection for the target sequence is critical in generating reliable and useful results. Several different approaches for probe design are reported in literature and an increasing number of bioinformatics tools are available for the same. However, based on the reported low accuracy, determining the hybridization efficiency of the probes is still a big computational challenge. Present study deals with the extraction of various novel features related to sequence composition, thermodynamics and secondary structure that may be essential for designing good probes. A feature selection method has been used to assess the relative importance of all these features. In this paper, we validate the importance of various features currently used for designing an oligonucleotide probe. Finally, a classification methodology is presented that can be used to predict the hybridization quality of a probe.
Starting Page	1
Ending Page	5
File Size	1039843
Page Count	5
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761758
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probes Thermodynamics Machine learning Artificial neural networks Springs Bioinformatics Asia Technological innovation Laboratories Feature extraction
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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