Protein Contact Map Prediction Based on ResNet and DenseNet.
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 ca...
| Published in: | BioMed Research International pp. 1 - 13 |
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| Main Authors: | , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
4/6/2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142741800&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142741800 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/6/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 142741800 142741800 142741800 10.1155/2020/7584968 142741800 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Protein Contact Map Prediction Based on ResNet and DenseNet. aug: au: Li, Zhong Lin, Yuele Elofsson, Arne Yao, Yuhua affil: School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China sug: subj: Neural Networks (Computer) Deep Learning Proteins Analysis Molecular Structure Evaluation Sequence Analysis Methods Models, Biological ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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