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+++ b/paper/paper.md
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+---
+title: 'biotmle: Targeted Learning for Biomarker Discovery'
+tags:
+  - targeted learning
+  - variable importance
+  - causal inference
+  - bioinformatics
+  - genomics
+  - R
+authors:
+  - name: Nima S. Hejazi
+    orcid: 0000-0002-7127-2789
+    affiliation: 1
+  - name: Weixin Cai
+    orcid: 0000-0003-2680-3066
+    affiliation: 1
+  - name: Alan E. Hubbard
+    orcid: 0000-0002-3769-0127
+    affiliation: 1
+affiliations:
+  - name: Division of Biostatistics, University of California, Berkeley
+    index: 1
+date: 26 July 2017
+bibliography: paper.bib
+---
+
+# Summary
+
+The `biotmle` package provides an implementation of a biomarker discovery
+methodology based on targeted minimum loss-based estimation (TMLE)
+[@vdl2011targeted] and a generalization of the moderated t-statistic of
+[@smyth2004linear], designed for use with biological sequencing data (e.g.,
+microarrays, RNA-seq). The statistical approach made available in this package
+relies on the use of TMLE to rigorously evaluate the association between a set
+of potential biomarkers and another variable of interest while adjusting for
+potential confounding from another set of user-specified covariates. The
+implementation is in the form of a package for the R language for statistical
+computing [@R].
+
+There are two principal ways in which the biomarker discovery techniques in
+the `biotmle` R package can be used: to evaluate the association between (1) a
+phenotypic measure (say, environmental exposure) and a biomarker of interest,
+and (2) an outcome of interest (e.g., survival status at a given time) and a
+biomarker measurement, both while controlling for background covariates (e.g.,
+BMI, age). By using an estimation procedure based on TMLE, the package produces
+results based on the Average Treatment Effect (ATE), a statistical parameter
+with a well-studied causal interpretation (see @vdl2011targeted for extended
+discussions), making the `biotmle` R package well-suited for applications in
+bioinformatics, epidemiology, and genomics.
+
+After adjusting our data set to be consistent with the expect input format --
+please consult the vignette accompanying the R package for details -- we would
+call the principal function of this R package: `biomarkertmle`.
+
+We would perform a moderated test on the output of the `biomarkertmle` function
+using the function `modtest_ic`.
+
+While the principal table of results produced by this R package matches those
+produced by the well-known `limma` R package [@smyth2005limma], there are also
+several plot methods made available for the `bioTMLE` S4 class -- subclassed
+from the popular `SummarizedExperiment` class -- introduced by this package
+[@huber2015orchestrating]. For illustrative purposes, we demonstrate the ouput
+of two such functions on anonymized experimental data below:
+
+![Heatmap visualizing the Average Treatment Effect contribution of a change in
+exposure to each biomarker of interest](figs/heatmap_biotmle.png)
+
+![Volcano plot visualizing the log fold change in the Average Treatment Effect
+against the raw p-value from the moderated t-test performed on each
+biomarker](figs/volcanoplot_biotmle.png)
+
+\newpage
+
+# References
+