\title{\textbf{The Partitioned LASSO-Patternsearch Algorithm 
with Application to Gene Expression Data}}
\author{\Large{Weiliang Shi, Grace Wahba, Rafael Irizarry, }\\ \Large{Hector Corrada Bravo, Stephen Wright}\\}
\maketitle
\begin{abstract}
  The Partitioned LASSO-Patternsearch algorithm is proposed to
  identify patterns of multiple dichotomous risk factors for outcomes
  of interest in genomic studies. A partitioning scheme is used to
  identify promising patterns by solving many LASSO-Patternsearch
  subproblems in parallel. All variables that survive this stage
  proceed to an aggregation stage where the most significant patterns
  are identified by solving a reduced LASSO-Patternsearch problem in
  just these variables. This approach was applied to genetic data sets
  with expression levels dichotomized by gene expression bar code.
  Most of the genes and second-order interactions thus selected and
  are known to be related to the outcomes. Cross-validation shows that
  the proposed method provides smaller models with better prediction
  accuracy, in comparison to several competing methodologies.
\end{abstract}
