Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Article Citation - WoS: 1Citation - Scopus: 1Identification of Candidate Biomarkers and Pathways in Breast Cancer by Differential Network Analysis(Inderscience Publishers, 2020) Mendi, Onur; Karahoca, AdemBreast cancer is one of the most malignant cancers in women worldwide. The aim of the present study was to explore the underlying biological mechanisms of breast cancer. For this purpose, we propose a novel framework to reveal mechanisms that drive disease progression in breast cancer by combining prior knowledge in the literature with differential networking methodology. Our integration framework has resulted in the most important genes and interactions by allowing ranking the breast cancer-specific gene network. YY1, SMARCA5, FOXM1, STAT4 and PTTG1 were found to be the most important genes in breast cancer. Functional and pathway enrichment analyses identified numerous pathways that may play a critical role in disease progression. Considering the success of the comparison of the results with the literature, the systemic lupus erythematosus pathway may be a potential target of breast cancer.Article Citation - WoS: 1Citation - Scopus: 2A Novel Genetic Algorithm-Based Improvement Model for Online Communities and Trust Networks(IOS Press, 2021-01-04) Bekmezci, ilker; Cimen, Egemen Berkic; Ermiş, MuratSocial network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.