Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model
  Learning with Application to Genomic Data Integration   Recent advances in biological research have seen the emergence of
high-throughput technologies with numerous applications that allow the study of
biological mechanisms at an unprecedented depth and scale. A large amount of
genomic data is now distributed through consortia like The Cancer Genome Atlas
(TCGA), where specific types of biological information on specific type of
tissue or cell are available. In cancer research, the challenge is now to
perform integrative analyses of high-dimensional multi-omic data with the goal
to better understand genomic processes that correlate with cancer outcomes,
e.g. elucidate gene networks that discriminate a specific cancer subgroups
(cancer sub-typing) or discovering gene networks that overlap across different
cancer types (pan-cancer studies). In this paper, we propose a novel mixed
graphical model approach to analyze multi-omic data of different types
(continuous, discrete and count) and perform model selection by extending the
Birth-Death MCMC (BDMCMC) algorithm initially proposed by
\citet{stephens2000bayesian} and later developed by
\cite{mohammadi2015bayesian}. We compare the performance of our method to the
LASSO method and the standard BDMCMC method using simulations and find that our
method is superior in terms of both computational efficiency and the accuracy
of the model selection results. Finally, an application to the TCGA breast
cancer data shows that integrating genomic information at different levels
(mutation and expression data) leads to better subtyping of breast cancers.

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The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph