Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6601
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Şirin, Utku | - |
dc.contributor.author | Erdoğdu, Utku | - |
dc.contributor.author | Tan, Mehmet | - |
dc.contributor.author | Polat, Faruk | - |
dc.contributor.author | Alhajj, Reda | - |
dc.date.accessioned | 2021-09-11T15:42:57Z | - |
dc.date.available | 2021-09-11T15:42:57Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | 11th IEEE International Conference on Machine Learning and Applications (ICMLA) -- DEC 12-15, 2012 -- Boca Raton, FL | en_US |
dc.identifier.isbn | 978-0-7695-4913-2; 978-1-4673-4651-1 | - |
dc.identifier.uri | https://doi.org/10.1109/ICMLA.2012.22 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6601 | - |
dc.description.abstract | The ever-growing need for gene-expression data analysis motivates studies in sample generation due to the lack of enough gene-expression data. It is common that there are thousands of genes but only tens or rarely hundreds of samples available. In this paper, we attempt to formulate the sample generation task as follows: first, building alternative Gene Regulatory Network (GRN) models; second, sampling data from each of them; and then filtering the generated samples using metrics that measure compatibility, diversity and coverage with respect to the original dataset. We constructed two alternative GRN models using Probabilistic Boolean Networks and Ordinary Differential Equations. We developed a multi-objective filtering mechanism based on the three metrics to assess the quality of the newly generated data. We presented a number of experiments to show effectiveness and applicability of the proposed multi-model framework. | en_US |
dc.description.sponsorship | IEEE, IEEE Comp Soc, AML&A, Florida Atlantic Univ, LexisNexis | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [110E179] | en_US |
dc.description.sponsorship | This work is partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. 110E179. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2012 11Th International Conference On Machine Learning And Applications (Icmla 2012), Vol 1 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | gene expression data | en_US |
dc.subject | sample generation | en_US |
dc.subject | multiple perspectives | en_US |
dc.subject | learning | en_US |
dc.subject | gene regulation modeling | en_US |
dc.subject | probabilistic boolean networks | en_US |
dc.subject | ordinary differential equations | en_US |
dc.title | Effective Enrichment of Gene Expression Data Sets | en_US |
dc.type | Conference Object | 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.startpage | 76 | en_US |
dc.identifier.endpage | 81 | en_US |
dc.authorid | 0000-0003-0509-9153 | - |
dc.authorid | 0000-0002-1741-0570 | - |
dc.identifier.wos | WOS:000427260500013 | en_US |
dc.identifier.scopus | 2-s2.0-84873602387 | en_US |
dc.institutionauthor | Tan, Mehmet | - |
dc.identifier.doi | 10.1109/ICMLA.2012.22 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 11th IEEE International Conference on Machine Learning and Applications (ICMLA) | en_US |
item.openairetype | Conference Object | - |
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 Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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