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Clustering binary fingerprint vectors with missing values for dna array data analysis

By Andres Figueroa, James Borneman and Tao Jiang

Abstract

Oligonucleotide fingerprinting is a powerful DNA array based method to characterize cDNA and ribosomal RNA gene (rDNA) libraries and has many applications including gene expression profiling and DNA clone classification. We are especially interested in the latter application. A key step in the method is the cluster analysis of fingerprint data obtained from DNA array hybridization experiments. Most of the existing approaches to clustering use (normalized) real intensity values and thus do not treat positive and negative hybridization signals equally (positive signals are much more emphasized). In this paper, we consider a discrete approach. Fingerprint data are first normalized and binarized using control DNA clones. Because there may exist unresolved (or missing) values in this binarizatio

Topics: oligonucleotide fingerprinting, cluster analysis, DNA clone classification, algorithm, DNA array
Year: 2003
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.6137
Provided by: CiteSeerX
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