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Protein Contact Map Prediction Based on ResNet and DenseNet
Joint Authors
Li, Zhong
Lin, Yuele
Elofsson, Arne
Yao, Yuhua
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-06
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Residue-residue contact prediction has become an increasingly important tool for modeling the three-dimensional structure of a protein when no homologous structure is available.
Ultradeep residual neural network (ResNet) has become the most popular method for making contact predictions because it captures the contextual information between residues.
In this paper, we propose a novel deep neural network framework for contact prediction which combines ResNet and DenseNet.
This framework uses 1D ResNet to process sequential features, and besides PSSM, SS3, and solvent accessibility, we have introduced a new feature, position-specific frequency matrix (PSFM), as an input.
Using ResNet’s residual module and identity mapping, it can effectively process sequential features after which the outer concatenation function is used for sequential and pairwise features.
Prediction accuracy is improved following a final processing step using the dense connection of DenseNet.
The prediction accuracy of the protein contact map shows that our method is more effective than other popular methods due to the new network architecture and the added feature input.
American Psychological Association (APA)
Li, Zhong& Lin, Yuele& Elofsson, Arne& Yao, Yuhua. 2020. Protein Contact Map Prediction Based on ResNet and DenseNet. BioMed Research International،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1137000
Modern Language Association (MLA)
Li, Zhong…[et al.]. Protein Contact Map Prediction Based on ResNet and DenseNet. BioMed Research International No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1137000
American Medical Association (AMA)
Li, Zhong& Lin, Yuele& Elofsson, Arne& Yao, Yuhua. Protein Contact Map Prediction Based on ResNet and DenseNet. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1137000
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1137000