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https://hdl.handle.net/20.500.11851/6897
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tan, Mehmet | - |
dc.contributor.author | Alshalalfa, Mohammed | - |
dc.contributor.author | Alhajj, Reda | - |
dc.contributor.author | Polat, Faruk | - |
dc.date.accessioned | 2021-09-11T15:44:08Z | - |
dc.date.available | 2021-09-11T15:44:08Z | - |
dc.date.issued | 2011 | en_US |
dc.identifier.issn | 1545-5963 | - |
dc.identifier.issn | 1557-9964 | - |
dc.identifier.uri | https://doi.org/10.1109/TCBB.2009.58 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6897 | - |
dc.description.abstract | Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior knowledge; one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets. | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) | en_US |
dc.description.sponsorship | Mehmet Tan's research is partially supported by the Scientific and Technological Research Council of Turkey. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE Computer Soc | en_US |
dc.relation.ispartof | IEEE-Acm Transactions On Computational Biology And Bioinformatics | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Gene regulatory networks | en_US |
dc.subject | transcription factors | en_US |
dc.subject | genes | en_US |
dc.subject | microarray data | en_US |
dc.subject | gene ontology | en_US |
dc.subject | prior knowledge-based learning | en_US |
dc.title | Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 8 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.startpage | 130 | en_US |
dc.identifier.endpage | 142 | en_US |
dc.authorid | 0000-0002-1741-0570 | - |
dc.authorid | 0000-0003-0509-9153 | - |
dc.identifier.wos | WOS:000283926400012 | en_US |
dc.identifier.scopus | 2-s2.0-78449277499 | en_US |
dc.institutionauthor | Tan, Mehmet | - |
dc.identifier.pmid | 21071802 | en_US |
dc.identifier.doi | 10.1109/TCBB.2009.58 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q2 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence Engineering | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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