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%%  LaTeX template for BioMed Central  %%
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\title{The Partitioned LASSO-Patternsearch Algorithm 
with Application to Gene Expression Data}
 
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%%   Weiliang Shi, Grace Wahba, Rafael Irizarry,Hector Corrada Bravo, Stephen Wright                                       %%
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\author{Weiliang Shi\correspondingauthor$^1$%
         \email{Weiliang Shi\correspondingauthor - weiliang.shi@sanofi-aventis.com}
       and 
         Grace Wahba$^2$%
         \email{Grace Wahba - wahba@stat.wisc.edu}%
      }
      

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\address{%
    \iid(1)Sanofi-Aventis, Cambridge, MA, USA\\
    \iid(2)Marine Ecology Department, Institute of Marine Sciences Kiel, %
        D\"{u}sternbrooker Weg 20, 24105 Kiel, Germany
}%

\maketitle

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\begin{abstract}
        % Do not use inserted blank lines (ie \\) until main body of text.
  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}



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\section*{Author's contributions}
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\section*{Acknowledgements}
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