Browsing by Author "Çetin-Atalay, Rengül"

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Open Access
Canlı hücre bölütlemesi için gözeticili öğrenme modeli
(IEEE Computer Society, 2014-04) Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem
Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds. © 2014 IEEE.
Open Access
Contrast enhancement of microscopy images using image phase information
(Institute of Electrical and Electronics Engineers, 2018) Çakır, Serdar; Kahraman, Deniz Cansen; Çetin-Atalay, Rengül; Çetin, A. Enis
Contrast enhancement is an important preprocessing step for the analysis of microscopy images. The main aim of contrast enhancement techniques is to increase the visibility of the cell structures and organelles by modifying the spatial characteristics of the image. In this paper, phase information-based contrast enhancement framework is proposed to overcome the limitations of existing image enhancement techniques. Inspired by the groundbreaking design of the phase contrast microscopy (PCM), the proposed image enhancement framework transforms the changes in image phase into the variations of magnitude to enhance the structural details of the image and to improve visibility. In addition, the concept of selective variation (SV) technique is introduced and enhancement parameters are optimized using SV. The experimental studies that were carried out on microscopy images show that the proposed scheme outperforms the baseline enhancement frameworks. The contrast enhanced images produced by the proposed method have comparable cellular texture structure as PCM images.
Open Access
Data and model driven hybrid approach to activity scoring of cyclic pathways
(Springer, Dordrecht, 2010) Işık, Z.; Atalay V.; Aykanat, Cevdet; Çetin-Atalay, Rengül
Analysis of large scale -omics data based on a single tool remains inefficient to reveal molecular basis of cellular events. Therefore, data integration from multiple heterogeneous sources is highly desirable and required. In this study, we developed a data- and model-driven hybrid approach to evaluate biological activity of cellular processes. Biological pathway models were taken as graphs and gene scores were transferred through neighbouring nodes of these graphs. An activity score describes the behaviour of a specific biological process was computed by owing of converged gene scores until reaching a target process. Biological pathway model based approach that we describe in this study is a novel approach in which converged scores are calculated for the cellular processes of a cyclic pathway. The convergence of the activity scores for cyclic graphs were demonstrated on the KEGG pathways. © 2011 Springer Science+Business Media B.V.
Open Access
DeepDistance: a multi-task deep regression model for cell detection in inverted microscopy images
(Elsevier, 2020) Koyuncu, Can Fahrettin; Güneşli, Gözde Nur; Çetin-Atalay, Rengül; Gündüz-Demir, Çigdem
This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression problem where multiple tasks are learned by using shared feature representations. To this end, it defines a secondary metric, normalized outer distance, to represent a different aspect of the problem and proposes to define its learning as complementary to the main cell detection task. In order to learn these two complementary tasks more effectively, the DeepDistance model designs a fully convolutional network (FCN) with a shared encoder path and end-to-end trains this FCN to concurrently learn the tasks in parallel. For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model that includes an auxiliary classification task and learns it in parallel to the two regression tasks by also sharing feature representations with them. DeepDistance uses the inner distances estimated by these FCNs in a detection algorithm to locate individual cells in a given image. In addition to this detection algorithm, this paper also suggests a cell segmentation algorithm that employs the estimated maps to find cell boundaries. Our experiments on three different human cell lines reveal that the proposed multi-task learning models, the DeepDistance model and its extended version, successfully identify the locations of cell as well as delineate their boundaries, even for the cell line that was not used in training, and improve the results of its counterparts.
Open Access
Development of a novel zebrafish xenograft model in ache mutants using liver cancer cell lines
(Nature Publishing Group, 2018) Avcı, M. Ender; Keskus, Ayşe Gökçe; Targen, Seniye; Işılak, M. Efe; Öztürk, Mehmet; Çetin-Atalay, Rengül; Adams, Michelle M.; Konu, Özlen
Acetylcholinesterase (AChE), an enzyme responsible for degradation of acetylcholine, has been identified as a prognostic marker in liver cancer. Although in vivo Ache tumorigenicity assays in mouse are present, no established liver cancer xenograft model in zebrafish using an ache mutant background exists. Herein, we developed an embryonic zebrafish xenograft model using epithelial (Hep3B) and mesenchymal (SKHep1) liver cancer cell lines in wild-type and ache sb55 sibling mutant larvae after characterization of cholinesterase expression and activity in cell lines and zebrafish larvae. The comparison of fluorescent signal reflecting tumor size at 3-days post-injection (dpi) revealed an enhanced tumorigenic potential and a reduced migration capacity in cancer cells injected into homozygous ache sb55 mutants when compared with the wild-type. Increased tumor load was confirmed using an ALU based tumor DNA quantification method modified for use in genotyped xenotransplanted zebrafish embryos. Confocal microscopy using the Huh7 cells stably expressing GFP helped identify the distribution of tumor cells in larvae. Our results imply that acetylcholine accumulation in the microenvironment directly or indirectly supports tumor growth in liver cancer. Use of this model system for drug screening studies holds potential in discovering new cholinergic targets for treatment of liver cancers.
Open Access
Gauss tabanlı modelleme kullanarak canlı hücre görüntülerinin öğreticisiz bölütlenmesi
(2011-04) Arslan, Salim; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem
The first step of targeted cancer drug development is to screen and determine drug candidates by in vitro measuring the effectiveness of the drugs. The tests developed for this purpose can be time consuming due to their procedures and cannot be conducted in every laboratory due to the required hardwares. On the other hand, an image-based screening test has a potential to be less time consuming since it can directly be carried out on the live cell images and to be more extensively used because of the availability of its required equipments and their relatively less expensive cost. With such an image-based test, it is possible to quantify the cell death by finding cellular regions and comparing it against the control group. In this work, we propose a new method that automatically locates the cellular regions by the unsupervised segmentation of live cell images. This method relies on approximately locating cellular regions and the background with gradient-based thresholding and morphological operators and then finding the final boundaries by modeling the gradient of these regions with Gaussians. Working on the images of different cell lines captured with different magnifications, our experiments show that the proposed method leads to promising results. © 2011 IEEE.
Open Access
Microscopic image classification using sparsity in a transform domain and Bayesian learning
(IEEE, 2011) Suhre, Alexander; Erşahin, Tülin; Çetin-Atalay, Rengül; Çetin, A. Enis
Some biomedical images show a large quantity of different junctions and sharp corners. It is possible to classify several types of biomedical images in a region covariance approach. Cancer cell line images are divided into small blocks and covariance matrices of image blocks are computed. Eigen-values of the covariance matrices are used as classification parameters in a Bayesian framework using the sparsity of the parameters in a transform domain. The efficiency of the proposed method over classification using standard Support Vector Machines (SVM) is demonstrated on biomedical image data. © 2011 EURASIP.
Open Access
Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance
(IEEE, 2012) Keskin, Furkan; Çetin, A. Enis; Erşahin, Tülin; Çetin-Atalay, Rengül
In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. WT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L 2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method. © 2012 IEEE.
Open Access
Molecular Biology of liver cancer
(Wiley-Blackwell Publishing, 2005) Öztürk, Mehmet; Çetin-Atalay, Rengül; Meyers, R. A.
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. Recent advances in the molecular profiling of HCC emphasize its intratumoral heterogeneity and reveal how cellular pathways are altered in favor of tumor progression. Malignant transformation of primary liver cancer is achieved through the acquisition of cancer hallmark capabilities that promote the uncontrolled proliferation of hepatocytes. In this review, the characteristics and acquired capabilities of human primary liver cancer, based on the HCC-specific genetic and epigenetic alterations, are described and discussed. Keywords: HBV A small DNA virus that infects hepatocytes in the liver, causing acute or chronic hepatitis; HCV A small RNA virus that infects hepatocytes in the liver, causing acute or chronic hepatitis; genome-wide expression; gene signatures; PI3K/AKT pathway; MAPK/ERK pathway; p53 ; hTERT
Open Access
A novel model-based method for feature extraction from protein sequences for classification
(IEEE, 2006) Saraç, Ö. S.; Atalay, V.; Çetin-Atalay, Rengül
Representation of amino-acid sequences constitutes the key point in classification of proteins into functional or structural classes. The representation should contain the biologically meaningful information hidden in the primary sequence of the protein. Conserved or similar subsequences are strong indicators of functional and structural similarity. In this study we present a feature mapping that takes into account the models of the subsequences of protein sequences. An expectation-maximization algorithm along with an HMM mixture model is used to cluster and learn the models of subsequences of a given set of proteins.
Open Access
Prediction of protein subcellular localization based on primary sequence data
(IEEE, 2004) Özarar, M.; Atalay, V.; Çetin-Atalay, Rengül
Subcellular localization is crucial for determining the functions of proteins. A system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order is designed. The approach for prediction is to find the most frequent motifs for each protein in a given class based on clustering via self organizing maps and then to use these most frequent motifs as features for classification by the help of multi layer perceptrons. This approach allows a classification independent of the length of the sequence. In addition to these, the use of a new encoding scheme is described for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. The statistical test results of the system is presented on a four class problem. P2SL achieves slightly higher prediction accuracy than the similar studies.
Open Access
A series of 2,4(1H,3H)-quinazolinedione derivatives: synthesis and biological evaluation as potential anticancer agents
(Bentham Science Publishers, 2016) Akgün, H.; Us-Yılmaz, D.; Çetin-Atalay, Rengül; Gözen, Damla
A series of 6,7-disubstituted-3-{2-[4-(substituted)piperazin-1-yl]-2-oxoethyl}quinazoline- 2,4(1H,3H)-dione derivatives (7-34) were synthesized and their structures were elucidated on the basis of analytical and spectral (UV, IR, 1H-NMR, 13C-NMR and MS) data. These synthesized compounds were evaluated for their in vitro cytotoxicities against a panel of three human cancer cell lines. According to the cytotoxicity screening results, 3-{2-[4-(4-chlorobenzyl)piperazin-1-yl]-2-oxoethyl} quinazoline-2,4(1H,3H)-dione (7) presented the highest activity against HUH-7, MCF-7 and HCT-116 cell line with the IC50 values of 2.5, 6.8 and 4.9 µM, respectively.
Open Access
Short time series microarray data analysis and biological annotation
(IEEE, 2008) Sökmen, Z.; Atalay, V.; Çetin-Atalay, Rengül
Significant gene list is the result of microarray data analysis should be explained for the purpose of biological functions. The aim of this study is to extract the biologically related gene clusters over the short time series microarray gene data by applying unsupervised methods and automatically perform biological annotation of those clusters. In the first step of the study, short time series microarray expression data is clustered according to similar expression profiles. After that, several biological data sources are integrated to get information related with the genes in one of those clusters and new sub-clusters are created by using this unified information. As a last step, biological annotation of gene sub-clusters is performed by using information related with those sub-clusters.
Open Access
Synthesis of novel 6-(4-substituted piperazine-1-yl)-9-(β-dribofuranosyl)purine derivatives, which lead to senescence-induced cell death in liver cancer cells
(ACS, 2012) Tunçbilek, M.; Güven, Ebru Bilget; Önder, T.; Çetin-Atalay, Rengül
Novel purine ribonucleoside analogues (9-13) containing a 4-substituted piperazine in the substituent at N-6 were synthesized and evaluated for their cytotoxicity on Huh7, HepG2, FOCUS, Mahlavu liver, MCF7 breast, and HCT116 colon carcinoma cell lines. The purine nucleoside analogues were analyzed initially by an anticancer drug-screening method based on a sulforhodamine B assay. Two nucleoside derivatives with promising cytotoxic activities (11 and 12) were further analyzed on the hepatoma cells. The N-6-(4-Trifluoromethylphenyl)piperazine analogue 11 displayed the best antitumor activity, with IC50 values between 5.2 and 9.2 mu M. Similar to previously described nucleoside analogues, compound 11 also interferes with cellular ATP reserves, possibly through influencing cellular kinase activities. Furthermore, the novel nucleoside analogue 11 was shown to induce senescence-associated cell death, as demonstrated by the SA beta-gal assay. The senescence-dependent cytotoxic effect of 11 was also confirmed through phosphorylation of the Rb protein by p15(INK4b) overexpression in the presence of this compound.
Open Access
Transcriptome profiles associated with selenium-deficiency-dependent oxidative stress identify potential diagnostic and therapeutic targets in liver cancer cells
(Scientific and Technical Research Council of Turkey - TUBITAK,Turkiye Bilimsel ve Teknik Arastirma Kurumu, 2021-04-20) Gözen, D.; Kahraman, D. C.; Narcı, K.; Shehwana, H.; Konu, Özlen; Çetin-Atalay, Rengül
Hepatocellular carcinoma (HCC) is one of the most common cancer types with high mortality rates and displays increased resistance to various stress conditions such as oxidative stress. Conventional therapies have low efficacies due to resistance and off-target effects in HCC. Here we aimed to analyze oxidative stress-related gene expression profiles of HCC cells and identify genes that could be crucial for novel diagnostic and therapeutic strategies. To identify important genes that cause resistance to reactive oxygen species (ROS), a model of oxidative stress upon selenium (Se) deficiency was utilized. The results of transcriptome-wide gene expression data were analyzed in which the differentially expressed genes (DEGs) were identified between HCC cell lines that are either resistant or sensitive to Se-deficiency-dependent oxidative stress. These DEGs were further investigated for their importance in oxidative stress resistance by network analysis methods, and 27 genes were defined to have key roles; 16 of which were previously shown to have impact on liver cancer patient survival. These genes might have Se-deficiency-dependent roles in hepatocarcinogenesis and could be further exploited for their potentials as novel targets for diagnostic and therapeutic approaches.