Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification of Noncoding RNA Elements Using Deep Convolutional Neural
  Networks   The paper proposes to employ deep convolutional neural networks (CNNs) to
classify noncoding RNA (ncRNA) sequences. To this end, we first propose an
efficient approach to convert the RNA sequences into images characterizing
their base-pairing probability. As a result, classifying RNA sequences is
converted to an image classification problem that can be efficiently solved by
available CNN-based classification models. The paper also considers the folding
potential of the ncRNAs in addition to their primary sequence. Based on the
proposed approach, a benchmark image classification dataset is generated from
the RFAM database of ncRNA sequences. In addition, three classical CNN models
have been implemented and compared to demonstrate the superior performance and
efficiency of the proposed approach. Extensive experimental results show the
great potential of using deep learning approaches for RNA classification.

37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol