IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction

Basit Öğe Kaydı
dc.authoridSabzekar, Mostafa/0000-0002-6886-1240
dc.authoridgormez, yasin/0000-0001-8276-2030
dc.contributor.authorGormez, Yasin
dc.contributor.authorSabzekar, Mostafa
dc.contributor.authorAydin, Zafer
dc.date.accessioned2024-10-26T18:07:20Z
dc.date.available2024-10-26T18:07:20Z
dc.date.issued2021
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThere is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.
dc.description.sponsorshipNational Center for High Performance Computing of Turkey (UHeM) [5004062016]
dc.description.sponsorshipThe experiments reported in this article were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources), the National Center for High Performance Computing of Turkey (UHeM) under project no 5004062016, and AGU HPC.
dc.identifier.doi10.1002/prot.26149
dc.identifier.endpage1288
dc.identifier.issn0887-3585
dc.identifier.issn1097-0134
dc.identifier.issue10
dc.identifier.pmid33993559
dc.identifier.scopus2-s2.0-85106266658
dc.identifier.scopusqualityQ1
dc.identifier.startpage1277
dc.identifier.urihttps://doi.org/10.1002/prot.26149
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29460
dc.identifier.volume89
dc.identifier.wosWOS:000653902500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofProteins-Structure Function and Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBayesian optimization
dc.subjectconvolutional neural network
dc.subjectdeep learning
dc.subjectgraph convolutional network
dc.subjectprotein secondary structure prediction
dc.titleIGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksyonu
Scopus İndeksli Yayınlar Koleksiyonu