Learning networks from high dimensional binary data : An application to genomic instability data

@inproceedings{Wang2009LearningNF,
  title={Learning networks from high dimensional binary data : An application to genomic instability data},
  author={Pei Wang and Dennis L. Chao},
  year={2009}
}
Genomic instability, the propensity of aberrations in chromosomes, plays a critical role in the development of many diseases. High throughput genotyping experiments have been performed to study genomic instability in diseases. The output of such experiments can be summarized as high dimensional binary vectors, where each binary variable records aberration status at one marker locus. It is of keen interest to understand how these aberrations interact with each other. In this paper, we propose a… CONTINUE READING
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