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Öğe The Adiponectin variants contribute to the genetic background of type 2 diabetes in Turkish population(Elsevier, 2014) Arikoglu, Hilal; Ozdemir, Hulya; Kaya, Dudu Erkoc; Ipekci, Suleyman Hilmi; Arslan, Ahmet; Kayis, Seyit Ali; Gonen, Mustafa SaitAdiponectin, an adipose tissue specific protein encoded by the Adiponectin gene, modulates insulin sensitivity and plays an important role in regulating energy homeostasis. Many studies have shown that single nucleotide polymorphisms (SNPs) in the Adiponectin gene are associated with low plasma Adiponectin levels, insulin resistance and an increased risk of type 2 diabetes mellitus. The aim of the present study was to evaluate the contribution of the Adiponectin gene polymorphisms in genetic background of type 2 diabetes in a Turkish population. In total, 169 unrelated and non-obese diabetic patients and 119 age- and BMI-matched nondiabetic individuals with no family history of diabetes were enrolled in this study. We detected a significant association between type 2 diabetes and two SNPs: SNP - 11391G>A. which is located in the promoter region of the Adiponectin gene, and SNP + 276G > T, which is found in intron 2 of the gene (P < 0.05). The silence SNP G15G ( + 45T > G) in exon 1 and SNP + 349A > G in intron 2 also showed a weak association with type 2 diabetes (P = 0.06 and P = 0.07, respectively), while SNPs - 3971A>G in intron 1 and Y111H, R112C and H241P in exon 3 showed no association (P > 0.05). In conclusion, these findings suggest that Adiponectin gene polymorphisms might be effective on susceptibility for type 2 diabetes development which emerged from the interactions between multiple genes, variants and environmental factors. (C) 2013 Elsevier B.V. All rights reserved.Öğe Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform(Elsevier Ireland Ltd, 2017) Akkoc, Betul; Arslan, Ahmet; Kok, HaticeBackground and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models. Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification. Results: Classification was performed using 30 different seeds for random generator values and 10 fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC). Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth. (C) 2017 Elsevier B.V. All rights reserved.Öğe Gender Determination from Teeth Images via Hybrid Feature Extraction Method(Springer International Publishing Ag, 2020) Uzbas, Betul; Arslan, Ahmet; Kok, Hatice; Acilar, Ayse MerveTeeth are a significant resource for determining the features of an unknown person, and gender is one of the important pieces of demographic information. For this reason, gender analysis from teeth is a current topic of research. Previous literature on gender determination have generally used values obtained through manual measurements of the teeth, gingiva, and lip area. However, such methods require extra effort and time. Furthermore, since sexual dimorphism varies among populations, it is necessary to know the optimum values for each population. This study uses a hybrid feature extraction method and a Support Vector Machine (SVM) for gender determination from teeth images. The study group was composed of 60 Turkish individuals (30 female, 30 male) between the ages of 19 and 27. Features were automatically extracted from the intraoral images through a hybrid method that combines two-dimensional Discrete Wavelet Transformation (DWT) and Principle Component Analysis (PCA). Classification was performed from these features through SVM. The system can be easily used on any population and can perform fast and low-cost gender determination without requiring any extra effort.Öğe Gray level co-occurrence and random forest algorithm-based gender determination with maxillary tooth plaster images(Pergamon-Elsevier Science Ltd, 2016) Akkoc, Betul; Arslan, Ahmet; Kok, HaticeGender is one of the intrinsic properties of identity, with performance enhancement reducing the cluster when a search is performed. Teeth have durable and resistant structure, and as such are important sources of identification in disasters (accident, fire, etc.). In this study, gender determination is accomplished by maxillary tooth plaster models of 40 people (20 males and 20 females). The images of tooth plaster models are taken with a lighting mechanism set-up. A gray level co-occurrence matrix of the image with segmentation is formed and classified via a Random Forest (RF) algorithm by extracting pertinent features of the matrix. Automatic gender determination has a 90% success rate, with an applicable system to determine gender from maxillary tooth plaster images. (C) 2016 Elsevier Ltd. All rights reserved.Öğe A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy(Univ Suceava, Fac Electrical Eng, 2013) Hacibeyoglu, Mehmet; Arslan, Ahmet; Kahramanli, SirzatUsually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method.