PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article Citation - WoS: 7Citation - Scopus: 11Protein Β-Sheet Prediction Using an Efficient Dynamic Programming Algorithm(Elsevier Sci Ltd, 2017-10) Sabzekar, Mostafa; Naghibzadeh, Mahmoud; Eghdami, Mandie; Aydin, ZaferPredicting the beta-sheet structure of a protein is one of the most important intermediate steps towards the identification of its tertiary structure. However, it is regarded as the primary bottleneck due to the presence of non-local interactions between several discontinuous regions in beta-sheets. To achieve reliable long-range interactions, a promising approach is to enumerate and rank all beta-sheet conformations for a given protein and find the one with the highest score. The problem with this solution is that the search space of the problem grows exponentially with respect to the number of beta-strands. Additionally, brute force calculation in this conformational space leads to dealing with a combinatorial explosion problem with intractable computational complexity. The main contribution of this paper is to generate and search the space of the problem efficiently to reduce the time complexity of the problem. To achieve this, two tree structures, called sheet-tree and grouping-tree, are proposed. They model the search space by breaking it into sub-problems. Then, an advanced dynamic programming is proposed that stores the intermediate results, avoids repetitive calculation by repeatedly uses them efficiently in successive steps and reduces the space of the problem by removing those intermediate results that will no longer be required in later steps. As a consequence, the following contributions have been made. Firstly, more accurate beta-sheet structures are found by searching all possible conformations, and secondly, the time complexity of the problem is reduced by searching the space of the problem efficiently which makes the proposed method applicable to predict beta-sheet structures with high number of beta-strands. Experimental results on the BetaSheet916 dataset showed significant improvements of the proposed method in both execution time and the prediction accuracy in comparison with the state-of-the-art beta-sheet structure prediction methods Moreover, we investigate the effect of different contact map predictors on the performance of the proposed method using BetaSheet1452 dataset. The source code is available at http://www.conceptsgate.com/BetaTop.rar. (C) 2017 Elsevier Ltd. All rights reserved.Article Citation - WoS: 27Citation - Scopus: 31Proteomic Fertility Markers in Ram Sperm(Elsevier, 2021-12) Hitit, Mustafa; Ozbek, Mehmet; Ayaz-Guner, Serife; Guner, Huseyin; Oztug, Merve; Bodu, Mustafa; Kaya, AbdullahPrecise estimation of ram fertility is important for sheep farming to sustain reproduction efficiency and profitability of production. There, however, is no conventional method to accurately predict ram fertility. The objective of this study, therefore, was to ascertain proteomic profiles of ram sperm having contrasting fertility phenotypes. Mature rams (n = 66) having greater pregnancy rates than average (89.4 +/- 7.2%) were assigned into relatively-greater fertility (GF; n = 31; 94.5 +/- 2.8%) whereas those with less-than-average pregnancy rates were assigned into a lesserfertility (LF; n = 25; 83.1 +/- 5.73%; P = 0.028) group. Sperm samples from the outlier greatestand least-fertility rams (n = 6, pregnancy rate; 98.4 +/- 1.8% and 76.1 +/- 3.9%) were used for proteomics assessments utilizing Label-free LC-MS/MS. A total of 997 proteins were identified, and among these, 840 were shared by both groups, and 57 and 93 were unique to GF and LF, respectively. Furthermore, 190 differentially abundant proteins were identified; the abundance of 124 was larger in GF while 66 was larger in LF rams. The GF ram sperm had 79 GO/pathway terms in ten major biological networks while there were 47 GO/pathway terms in six biological networks in sperm of LF rams. Accordingly, differential abundances of sperm proteins between sperm of GF and LF rams were indicative of functional implications of sperm proteome on male fertility. The results of this study emphasize there are potential protein markers for evaluation of semen quality and estimation of ram sperm fertilizing capacity.Article Citation - WoS: 14Citation - Scopus: 15Image-Analysis Based Readout Method for Biochip: Automated Quantification of Immunomagnetic Beads, Micropads and Patient Leukemia Cell(Pergamon-Elsevier Science Ltd, 2020-06) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Dogan, Refika S.; Yilmaz, BulentFor diagnosing and monitoring the progress of cancer, detection and quantification of tumor cells is utmost important. Beside standard bench top instruments, several biochip-based methods have been developed for this purpose. Our biochip design incorporates micron size immunomagnetic beads together with micropad arrays, thus requires automated detection and quantification of not only cells but also the micropads and the immunomagnetic beads. The main purpose of the biochip is to capture target cells having different antigens simultaneously. In this proposed study, a digital image processing-based method to quantify the leukemia cells, immunomagnetic beads and micropads was developed as a readout method for the biochip. Color, size-based object detection and object segmentation methods were implemented to detect structures in the images acquired from the biochip by a bright field optical microscope. It has been shown that manual counting and flow cytometry results are in good agreement with the developed automated counting. Average precision is 85 % and average error rate is 13 % for all images of patient samples, average precision is 99 % and average error rate is 1% for cell culture images. With the optimized micropad size, proposed method can reach up to 95 % precision rate for patient samples with an execution time of 90 s per image.