NDLI: Data Analysis of Arabidopsis Tiling Array

Content Provider	IEEE Xplore Digital Library
Author	Guoli Ji Shanshan Wu Xiaohui Wu Denghui Xing Qingshun Quinn Li
Copyright Year	2009
Abstract	DNA tiling microarray technology has become a major bioinformatics tool for genomic research. Due to the high-density, high-throughput characteristics, tiling array can help to study gene expression and to explore the mystery of life from genome level. However, due to its data volume and complexity, the analysis of tiling array data is not streamlined yet. Although some dynamic programming approaches have been successfully applied to yeast tiling array data, the segmentation problem is considerably more challenging for the genomes of higher eukaryotes, such as Arabidopsis. In this paper, we applied a new machine learning method combining the advantages of Hidden Markov (HM) models and Support Vector Machines (SVM) to deal with the Arabidopsis tiling array data by adopting the probe filtering and normalization of wild type samples to identify gene structures.
Starting Page	3637
Ending Page	3640
File Size	379789
Page Count	4
File Format	PDF
ISBN	9781424449095
e-ISBN	9781424457281
DOI	10.1109/ICISE.2009.444
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Fungi Learning systems Data analysis Genomics Hidden Markov models DNA Dynamic programming Gene expression Bioinformatics
Content Type	Text
Resource Type	Article

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