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.