[WIP] Outline the Study section

references/tags.tsv

@@ -1,2 +1,6 @@
 tag	citation
 Zhou2015_deep_sea	doi:10.1038/nmeth.3547
+Chen2015_trans_species	doi:10.1093/bioinformatics/btv315
+Arvaniti2016_rare_subsets	doi:10.1101/046508
+Angermueller2016_single_methyl	doi:10.1101/055715
+Shaham2016_batch_effects	arxiv:1610.04181

sections/04_study.md

@@ -0,0 +1,90 @@
+## How is deep learning used to study basic biological processes in a manner that may provide future insights into human disease?
+
+*The (awkward) placeholder section title is intended to help define the scope.
+We do not want this section to become a miscellaneous collection of everything
+that does not fit in Categorize and Treat.*
+
+*One proposal is that we organize this roughly by what is being predicted,
+which will generally correspond to the types of data being used.  For each
+sub-section we can quickly introduce the prediction problem and cite some
+examples of the relevance to disease.  Hypothetically, if we had an algorithm
+that produced perfect predictions on the task, what would we learn and how
+could those predictions be used?*
+
+*Existing reviews could be mentioned briefly.*
+
+*It may not fit here, but there could be a general discussion of why different
+neural network architectures are particularly well-suited for different types
+of input data.  For example, CNNs and RNNs for 1-dimensional data are used
+in several categories below.*
+
+*A few suggestions for sub-sections follow.  Some of these could be left out
+because our goal is not an exhaustive enumeration of methods.  Some
+are important areas of biology, but there may not be much deep learning-
+specific content to present.  Others may be important areas where we lack
+expertise, in which case we may acknowledge the application area but not
+dive into merits or weaknesses of individual methods.*
+
+### Gene expression
+
+*Predicting gene expression levels and unsupervised approaches for learning
+from gene expression.  Those could be divided into separate sub-sections.*
+
+### Splicing
+
+*A separate section from general gene expression section above.*
+
+### Transcription factors and RNA-binding proteins
+
+*Existing reviews have covered some of these papers rather well and we do not
+want to repeat what has already been well-stated elsewhere.  This could
+be split into two sub-sections or kept very brief.*
+
+### Promoters, enhancers, and related epigenomic tasks
+
+*We may want to be selective about what we discuss and not list every
+application in this area.*
+
+### Micro-RNA binding
+
+*miRNAs are important biologically, but have neural networks produced anything
+particularly notable in this area?*
+
+### Protein secondary and tertiarty structure
+
+*We have not surveyed this area comprehensively yet.*
+
+### Signaling
+
+*There is not much content here.  Can [@tag:Chen2015_trans_species] be covered
+elsewhere?*
+
+### Cellular phenotypes
+
+*These are primarily imaging-based phenotypes.  We have not surveyed this area
+very comprehensively.  We could decide to not make imaging a primary focus,
+refer to existing reviews, and mention only a few particularly noteworthy
+representative papers.  Alternatively, we need to expand our literature review
+and summaries immediately if someone wants to be responsible for this
+sub-section.*
+
+*Transfer learning from non-biological datasets to biological imaging
+data could fit here, and that does seem like an important topic.  Or
+transfer learning could be a more general topic for the Discussion section.*
+
+### Single-cell
+
+*There are not many neural network papers in this area (yet), unless we count
+imaging applications.  But there is still plenty to discuss.  The existing
+methods [@tag:Arvaniti2016_rare_subsets @tag:Angermueller2016_single_methyl]
+use interesting network architectures to approach single-cell data.
+[@tag:Shaham2016_batch_effects] could fit here.*
+
+### Metagenomics
+
+*@gailrosen will write this*
+
+### Sequencing and variant calling
+
+*We have one nanopore paper in the issues and very recent work on variant calling
+that looks worthy of inclusion.*