Using machine learning classifiers to identify the critical proteins in Down syndrome

dc.contributor.authorDağ, Tamer
dc.contributor.authorDağ, Tamer
dc.date.accessioned2019-06-28T11:11:15Z
dc.date.available2019-06-28T11:11:15Z
dc.date.issued2018
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractPharmacotherapies of intellectual disability (ID) are largely unknown as the abnormalities at the complex molecular level which causes ID are difficult to understand. Down syndrome (DS) which is the prevalent cause of ID and caused by an extra copy of the human chromosome21 (Hsa21) has been investigated on protein levels by using the Ts65Dn mouse model of DS which are orthologs of %50 of Hsa21 classical protein coding genes. Recent works have applied the classification methods to understand critical factors in DS as it is believed that the problem was naturally related to classification problem since the determination of proteins discriminatory between classes of mice was required. In this study we apply forward feature selection method to identify correlated proteins and their interactions in DS. After identification we report supervised learning model of expression levels of selected proteins in order to understand the critical proteins for diagnosing and explaining DS. The proposed technique depicts optimum classification results achieved by optimizing parameters with grid search. When compared with the former work our classification results give higher accuracy. © 2018 Association for Computing Machinery.en_US]
dc.identifier.citation4
dc.identifier.doi10.1145/3290818.3290831en_US
dc.identifier.endpage54
dc.identifier.isbn9781450365529
dc.identifier.scopus2-s2.0-85061092572en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage51en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/1516
dc.identifier.wosqualityN/A
dc.institutionauthorKulan, Handanen_US
dc.institutionauthorDağ, Tameren_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDown syndromeen_US
dc.subjectLearningen_US
dc.subjectSupervised learningen_US
dc.titleUsing machine learning classifiers to identify the critical proteins in Down syndromeen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
relation.isAuthorOfPublication6e6ae480-b76e-48a0-a543-13ef44f9d802
relation.isAuthorOfPublication.latestForDiscovery6e6ae480-b76e-48a0-a543-13ef44f9d802

Files