Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised and Supervised Structure Learning for Protein Contact
  Prediction   Protein contacts provide key information for the understanding of protein
structure and function, and therefore contact prediction from sequences is an
important problem. Recent research shows that some correctly predicted
long-range contacts could help topology-level structure modeling. Thus, contact
prediction and contact-assisted protein folding also proves the importance of
this problem. In this thesis, I will briefly introduce the extant related work,
then show how to establish the contact prediction through unsupervised
graphical models with topology constraints. Further, I will explain how to use
the supervised deep learning methods to further boost the accuracy of contact
prediction. Finally, I will propose a scoring system called diversity score to
measure the novelty of contact predictions, as well as an algorithm that
predicts contacts with respect to the new scoring system.

9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph