Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
3 results
Search Results
Article Citation - WoS: 1Citation - Scopus: 2WSA-Supplements and Proper Classes(MDPI, 2022-08-17) Demirci, Yilmaz Mehmet; Turkmen, ErgulIn this paper, we introduce the concept of wsa-supplements and investigate the objects of the class of short exact sequences determined by wsa-supplement submodules, where a submodule U of a module M is called a wsa-supplement in M if there is a submodule V of M with U + V = M and U boolean AND V is weakly semiartinian. We prove that a module M is weakly semiartinian if and only if every submodule of M is a wsa-supplement in M. We introduce CC-rings as a generalization of C-rings and show that a ring is a right CC-ring if and only if every singular right module has a crumbling submodule. The class of all short exact sequences determined by wsa-supplement submodules is shown to be a proper class which is both injectively and co-injectively generated. We investigate the homological objects of this proper class along with its relation to CC-rings.Article Citation - WoS: 2Citation - Scopus: 2On a Class of Harada Rings(De Gruyter Poland Sp. z o.o., 2022-01-01) Turkmen, Burcu Nisanci; Demirci, Yilmaz MehmetIn this study, inspired by the definition and a previous study [F. Eryilmaz, SS -lifting modules and rings, Miskolc Math. Notes 22 (2021), no. 2, 655-662], left Harada rings are adapted to ss-Harada rings, and the important properties of these rings are provided. The characterization of a left ss-Harada ring R with R left perfect and Rad(R) included in Soc(RR) was found with the help of strongly local R-modules.Article Citation - WoS: 24Citation - Scopus: 24Circular RNA-MicroRNA Interaction Predictions in SARS-CoV Infection(Walter de Gruyter Gmbh, 2021-03-01) Demirci, Yilmaz Mehmet; Demirci, Muserref Duygu Sacar; Saçar Demirci, Müşerref DuyguDifferent types of noncoding RNAs like MicroRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis work flow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.