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

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

2009-08-26

arxiv.org/abs/0908.3882v1

Statistics

Methodology

Scientific paper

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 novel method, \texttt{LogitNet}, to infer the interactions among aberration events. The method is based on penalized logistic regression with an extension to account for spatial correlation in the genomic instability data. We conduct extensive simulation studies and show that the proposed method performs well in the situations considered. Finally, we illustrate the method using genomic instability data from breast cancer samples.

Affiliated with

Also associated with

No associations

Rating

Learning networks from high dimensional binary data: An application to genomic instability data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Learning networks from high dimensional binary data: An application to genomic instability data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Learning networks from high dimensional binary data: An application to genomic instability data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-625760