Browsing by Subject "Cancer cell lines"

Now showing 1 - 6 of 6

Open Access
Carcinoma cell line discrimination in microscopic images using unbalanced wavelets
(IEEE, 2012-03) Keskin, Furkan; Suhre, Alexander; Erşahin, Tüli,; Çetin Atalay, Rengül; Çetin, A. Enis
Cancer cell lines are widely used for research purposes in laboratories all over the world. In this paper, we present a novel method for cancer cell line image classification, which is very costly by conventional methods. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a correlation descriptor utilizing the fractional unbalanced wavelet transform coefficients and several morphological attributes as pixel features is computed. Directionally selective textural features are preferred primarily because of their ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines. A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel is employed for final classification. Over a dataset of 280 images, we achieved an accuracy of 88.2%, which outperforms the classical correlation based methods. © 2012 IEEE.
Open Access
Comparative evaluation of in vitro cytotoxic effects among parent abietyl alcohol and novel fatty acid ester derivatives against MCF7 and hepatocellular carcinoma cell lines
(Pakistan Journal of Pharmaceutical Sciences, 2014) Mustufa, Muhammad Ayaz; Aslam, A.; Özen, Çiğdem; Hashmi, I. A.; Naqvi, N.; Öztürk, Mehmet; Ali, F. I.
Synthesis of twelve hitherto unreported esters of abietyl alcohol and screening of these esters against four cancer cell lines including one breast cancer line MCF7 and four hepatocellular carcinoma cell lines (HCC) Huh7, Hep3B, Snu449 and Plc has been determined using SRB assay. The Cell cycle progression showed changes in cellular behaviour after 48 and 72 hours in MCF7 and Huh7 cell lines. Abietyl alcohol was obtained from the reduction of abietic acid, a tricyclic diterpene, isolated from oleoresin of Pinus longifolia Roxberghii.
Open Access
MatchMaker: A deep learning framework for drug synergy prediction
(IEEE, 2021-06-04) Kuru, Halil İbrahim; Taştan, Ö.; Çiçek, Ercüment
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to 15% correlation and 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker
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
A multiplication-free framework for signal processing and applications in biomedical image analysis
(IEEE, 2013) Suhre, A.; Keskin F.; Ersahin, T.; Cetin-Atalay, R.; Ansari, R.; Cetin, A.E.
A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix. © 2013 IEEE.