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dc.contributor.authorAydin, Zafer
dc.contributor.authorKaynar, Oguz
dc.contributor.authorGormez, Yasin
dc.date.accessioned2021-04-27T08:41:49Z
dc.date.available2021-04-27T08:41:49Z
dc.date.issued2018en_US
dc.identifier.issn0219-7200
dc.identifier.issn1757-6334
dc.identifier.urihttps://doi.org/10.1142/S0219720018500208
dc.identifier.urihttps://hdl.handle.net/20.500.12573/684
dc.descriptionThis work is supported by Grant 113E550 from 3501 TUBITAK National Young Researchers Career Award.en_US
dc.description.abstractSecondary structure and solvent accessibility prediction provide valuable information for estimating the three dimensional structure of a protein. As new feature extraction methods are developed the dimensionality of the input feature space increases steadily. Reducing the number of dimensions provides several advantages such as faster model training, faster prediction and noise elimination. In this work, several dimensionality reduction techniques have been employed including various feature selection methods, autoencoders and PCA for protein secondary structure and solvent accessibility prediction. The reduced feature set is used to train a support vector machine at the second stage of a hybrid classifier. Cross-validation experiments on two difficult benchmarks demonstrate that the dimension of the input space can be reduced substantially while maintaining the prediction accuracy. This will enable the incorporation of additional informative features derived for predicting the structural properties of proteins without reducing the accuracy due to overfitting.en_US
dc.description.sponsorshipTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) 113E550 3501en_US
dc.language.isoengen_US
dc.publisherIMPERIAL COLLEGE PRESS, 57 SHELTON ST, COVENT GARDEN, LONDON WC2H 9HE, ENGLANDen_US
dc.relation.isversionof10.1142/S0219720018500208en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectautoencoderen_US
dc.subjectdimension reductionen_US
dc.subjectfeature selectionen_US
dc.subjectsolvent accessibility predictionen_US
dc.subjectSecondary structure predictionen_US
dc.titleDimensionality reduction for protein secondary structure and solvent accesibility predictionen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volumeVolume: 16 Special Issue: SIen_US
dc.identifier.issue5en_US
dc.relation.journalJOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGYen_US
dc.relation.tubitak113E550 3501
dc.relation.publicationcategoryMakale - Uluslararası - Editör Denetimli Dergien_US


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