Researcher:
Yüksektepe, Fadime Üney

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Fadime Üney

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Yüksektepe

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Yüksektepe, Fadime Üney

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2005 - 20085

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    Prediction of secondary structures of proteins using a two-stage method
    (Pergamon-Elsevier Science Ltd, 2008) Department of Industrial Engineering; N/A; Department of Industrial Engineering; Yüksektepe, Fadime Üney; Yılmaz, Özlem; Türkay, Metin; Researcher; Master Student; Faculty Member; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 24956
    Protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities of organisms. The experimental methods used to determine the structures of proteins demand sophisticated equipment and time. A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results. However, prediction accuracies of these methods rarely exceed 70%. In this paper, a novel two-stage method to predict the location of secondary structure elements in a protein using the primary structure data only is presented. In the first stage of the proposed method, the folding type of a protein is determined using a novel classification approach for multi-class problems. The second stage of the method utilizes data available in the Protein Data Bank and determines the possible location of secondary structure elements in a probabilistic search algorithm. It is shown that the average accuracy of the predictions is 74.1 % on a large structure dataset.
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    Prediction of folding type of proteins using mixed-integer linear programming
    (Elsevier Science Bv, 2005) Department of Industrial Engineering; Department of Industrial Engineering; N/A; Türkay, Metin; Yüksektepe, Fadime Üney; Yılmaz, Özlem; Faculty Member; Researcher; Master Student; Department of Industrial Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/A
    Proteins are classified into four main structural classes by considering their amino acid compositions. Traditional approaches that use hyperplanes to partition data sets into two groups perform poorly due to the existence of four classes. Therefore, a novel method that uses mixed-integer programming is developed to overcome difficulties and inconsistencies of these traditional approaches. Mixed-integer programming (MIP) allows the use of hyper-boxes in order to define the boundaries of the sets that include all or some of the points in that class. For this reason, the efficiency and accuracy of data classification with MIP approach can be improved dramatically compared to the traditional methods. The efficiency of the proposed approach is illustrated on a training set of 120 proteins (30 from each type). The prediction results and their validation are also examined.
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    Prediction of secondary structures of proteins using a two-stage method
    (Elsevier Science Bv, 2006) Department of Industrial Engineering; N/A; N/A; Türkay, Metin; Yılmaz, Özlem; Yüksektepe, Fadime Üney; Faculty Member; Master Student; PhD Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/A
    Protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities in any organism. the experimental methods used to determine the structures of proteins demand sophisticated equipment and time. in order to overcome the shortcomings of the experimental methods, A host of algorithms aimed at predicting the location of secondary structure elements using statistical and computational methods are developed. However, prediction accuracies of these methods rarely exceeded 70%. in this paper a novel two-stage method to predict the location of secondary structure elements in a protein using the primary structure data only is presented. in the first stage of the proposed method, folding type of a protein is determined using a novel classification model for multi-class problems. the second stage of the method utilizes data available in the Protein Data Bank and determines the possible location of secondary structure elements in a probabilistic search algorithm. It is shown that the average accuracy of the predictions increased to 74.1%.
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    A mixed-integer programming approach to multi-class data classification problem
    (Elsevier Science Bv, 2006) Department of Industrial Engineering; Department of Industrial Engineering; Yüksektepe, Fadime Üney; Türkay, Metin; Researcher; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 108243; 24956
    This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer programming model is developed for representing existence of hyper-boxes and their boundaries. In addition, the relationships among the discrete decisions in the model are represented using propositional logic and then converted to their equivalent integer constraints using Boolean algebra. The proposed approach for multi-class data classification is illustrated on an example problem. The efficiency of the proposed method is tested on the well-known IRIS data set. The computational results on the illustrative example and the IRIS data set show that the proposed method is accurate and efficient on multi-class data classification problems.
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    PublicationOpen Access
    Classification of drug molecules considering their IC(50) values using mixed-integer linear programming based hyper-boxes method
    (BioMed Central, 2008) Department of Industrial Engineering; Department of Chemical and Biological Engineering; Armutlu, Pelin; Özdemir, Muhittin Emre; Yüksektepe, Fadime Üney; Kavaklı, İbrahim Halil; Türkay, Metin; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; N/A; N/A; N/A; 40319; 24956
    Background: A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC(50) values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. Results: We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC(50) values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naive Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Conclusion: Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.