Evidence map›Paper›PMID 35083336›Full record

ArticleBioMed research international2022

Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map.

Weizhong Lu, Nan Zhou, Yijie Ding, Hongjie Wu, Yu Zhang, Qiming Fu, Haiou Li

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 46% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Weizhong LuSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
Nan ZhouSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
Yijie DingSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.ORCID https://orcid.org/0000-0003-2911-7643
Hongjie WuSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
Yu ZhangSuzhou Industrial Park Institute of Services Outsourcing, Suzhou, China.
Qiming FuSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.ORCID https://orcid.org/0000-0001-9757-4323
Haiou LiProvincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China.
Suzhou University of Science and Technology · CNSoochow University · CNSuzhou Industrial Park Institute of Services Outsourcing · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset.

Indexed as

DNA-Binding ProteinsNeural Networks, ComputerDNA-Binding Proteins

Identifiers

PMID35083336
PMCPMC8786515
OpenAlexW4206645921

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.