PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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Article Citation - WoS: 55Citation - Scopus: 56Evaluation of an Artificial Intelligence System for Diagnosing Scaphoid Fracture on Direct Radiography(Springer Verlag, 2020) Özkaya, Emre; Topal, Fatih Esad; Bulut, Tuğrul; Gürsoy, Merve; Özuysal, Mustafa; Karakaya, ZeynepPurpose The aim of this study is to determine the diagnostic performance of artificial intelligence with the use of convolutional neural networks (CNN) for detecting scaphoid fractures on anteroposterior wrist radiographs. The performance of the deep learning algorithm was also compared with that of the emergency department (ED) physician and two orthopaedic specialists (less experienced and experienced in the hand surgery). Methods A total 390 patients with AP wrist radiographs were included in the study. The presence/absence of the fracture on radiographs was confirmed via CT. The diagnostic performance of the CNN, ED physician and two orthopaedic specialists (less experienced and experienced) as measured by AUC, sensitivity, specificity, F-Score and Youden index, to detect scaphoid fractures was evaluated and compared between the groups. Results The CNN had 76% sensitivity and 92% specificity, 0.840 AUC, 0.680 Youden index and 0.826Fscore values in identifying scaphoid fractures. The experienced orthopaedic specialist had the best diagnostic performance according to AUC. While CNN's performance was similar to a less experienced orthopaedic specialist, it was better than the ED physician. Conclusion The deep learning algorithm has the potential to be used for diagnosing scaphoid fractures on radiographs. Artificial intelligence can be useful for scaphoid fracture diagnosis particularly in the absence of an experienced orthopedist or hand surgeon.Article Citation - WoS: 1Creation of Mutants by Using Centrality Criteria in Social Network Analysis(PeerJ Inc., 2020) Takan, SavaşMutation testing is a method widely used to evaluate the effectiveness of the test suite in hardware and software tests or to design new software tests. In mutation testing, the original model is systematically mutated using certain error assumptions. Mutation testing is based on well-defined mutation operators that imitate typical programming errors or which form highly successful test suites. The success of test suites is determined by the rate of killing mutants created through mutation operators. Because of the high number of mutants in mutation testing, the calculation cost increases in the testing of finite state machines (FSM). Under the assumption that each mutant is of equal value, random selection can be a practical method of mutant reduction. However, in this study, it was assumed that each mutant did not have an equal value. Starting from this point of view, a new mutant reduction method was proposed by using the centrality criteria in social network analysis. It was assumed that the central regions selected within this frame were the regions from where test cases pass the most. To evaluate the proposed method, besides the feature of detecting all failures related to the model, the widely-used W method was chosen. Random and proposed mutant reduction methods were compared with respect to their success by using test suites. As a result of the evaluations, it was discovered that mutants selected via the proposed reduction technique revealed a higher performance. Furthermore, it was observed that the proposed method reduced the cost of mutation testing.Article Citation - WoS: 1Citation - Scopus: 1Dnmso; an Ontology for Representing De Novo Sequencing Results From Tandem-Ms Data(PeerJ Inc., 2020) Takan, Savaş; Allmer, JensFor the identification and sequencing of proteins, mass spectrometry (MS) has become the tool of choice and, as such, drives proteomics. MS/MS spectra need to be assigned a peptide sequence for which two strategies exist. Either database search or de novo sequencing can be employed to establish peptide spectrum matches. For database search, mzIdentML is the current community standard for data representation. There is no community standard for representing de novo sequencing results, but we previously proposed the de novo markup language (DNML). At the moment, each de novo sequencing solution uses different data representation, complicating downstream data integration, which is crucial since ensemble predictions may be more useful than predictions of a single tool. We here propose the de novo MS Ontology (DNMSO), which can, for example, provide many-to-many mappings between spectra and peptide predictions. Additionally, an application programming interface (API) that supports any file operation necessary for de novo sequencing from spectra input to reading, writing, creating, of the DNMSO format, as well as conversion from many other file formats, has been implemented. This API removes all overhead from the production of de novo sequencing tools and allows developers to concentrate on algorithm development completely. We make the API and formal descriptions of the format freely available at https://github.com/savastakan/dnmso.Article Citation - WoS: 17Citation - Scopus: 19Pro-Metastatic Functions of Notch Signaling Is Mediated by Cyr61 in Breast Cells(Elsevier, 2020) Küçükköse, Cansu; Efe, Eda; Günyüz, Zehra Elif; Fıratlıgil, Burcu; Doğan, Hülya; Yalçın Özuysal, Özden; İlhan, MustafaMetastasis is the main cause of cancer related deaths, and unfolding the molecular mechanisms underlying metastatic progression is critical for the development of novel therapeutic approaches. Notch is one of the key signaling pathways involved in breast tumorigenesis and metastasis. Notch activation induces pro-metastatic processes such as migration, invasion and epithelial to mesenchymal transition (EMT). However, molecular mediators working downstream of Notch in these processes are not fully elucidated. CYR61 is a secreted protein implicated in metastasis, and its inhibition by a monoclonal antibody suppresses metastasis in xenograft breast tumors, indicating the clinical importance of CYR61 targeting. Here, we aimed to investigate whether CYR61 works downstream of Notch in inducing pro-metastatic phenotypes in breast cells. We showed that CYR61 expression is positively regulated by Notch activity in breast cells. Notch1-induced migration, invasion and anchorage independent growth of a normal breast cell line, MCF10A, were abrogated by CYR61 silencing. Furthermore, upregulation of core EMT markers upon Notch1-activation was impaired in the absence of CYR61. However, reduced migration and invasion of highly metastatic cell line, MDA MB 231, cells upon Notch inhibition was not dependent on CYR61 downregulation. In conclusion, we showed that in normal breast cell line MCF10A, CYR61 is a mediator of Notch1-induced pro-metastatic phenotypes partly via induction of EMT. Our results imply CYR61 as a prominent therapeutic candidate for a subpopulation of breast tumors with high Notch activity.