Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in
  Cancer   The emerging field of precision oncology relies on the accurate pinpointing
of alterations in the molecular profile of a tumor to provide personalized
targeted treatments. Current methodologies in the field commonly include the
application of next generation sequencing technologies to a tumor sample,
followed by the identification of mutations in the DNA known as somatic
variants. The differentiation of these variants from sequencing error poses a
classic classification problem, which has traditionally been approached with
Bayesian statistics, and more recently with supervised machine learning methods
such as neural networks. Although these methods provide greater accuracy,
classic neural networks lack the ability to indicate the confidence of a
variant call. In this paper, we explore the performance of deep Bayesian neural
networks on next generation sequencing data, and their ability to give
probability estimates for somatic variant calls. In addition to demonstrating
similar performance in comparison to standard neural networks, we show that the
resultant output probabilities make these better suited to the disparate and
highly-variable sequencing data-sets these models are likely to encounter in
the real world. We aim to deliver algorithms to oncologists for which model
certainty better reflects accuracy, for improved clinical application. By
moving away from point estimates to reliable confidence intervals, we expect
the resultant clinical and treatment decisions to be more robust and more
informed by the underlying reality of the tumor molecular profile.

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The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics