Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of TensorFlow to recognition of visualized results of
  fragment molecular orbital (FMO) calculations   We have applied Google's TensorFlow deep learning toolkit to recognize the
visualized results of the fragment molecular orbital (FMO) calculations.
Typical protein structures of alpha-helix and beta-sheet provide some
characteristic patterns in the two-dimensional map of inter-fragment
interaction energy termed as IFIE-map (Kurisaki et al., Biophys. Chem. 130
(2007) 1). A thousand of IFIE-map images with labels depending on the
existences of alpha-helix and beta-sheet were prepared by employing 18 proteins
and 3 non-protein systems and were subjected to training by TensorFlow.
Finally, TensorFlow was fed with new data to test its ability to recognize the
structural patterns. We found that the characteristic structures in test
IFIE-map images were judged successfully. Thus the ability of pattern
recognition of IFIE-map by TensorFlow was proven.

31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat