Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Subject: search.filters.subject.Gene ExpressionWoS Q: search.filters.wosQuartile.N/A

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Now showing 1 - 6 of 6
  • Conference Object
    Citation - Scopus: 2
    miRcorrNetPro: Unraveling Algorithmic Insights Through Cross-Validation in Multi-Omics Integration for Comprehensive Data Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2023-12-05) Ünlü Yazici, Miray; Yousef, Malik; Marron, J. S.; Bakir-Güngör, Burcu; Yazici, Miray Unlu
    High throughput -omics technologies facilitate the investigation of regulatory mechanisms of complex diseases. Along this line, scientists develop promising tools and methods to extend our understanding at the molecular and functional levels. To this end, miRcorrNet tool performs integrative analysis of MicroRNA (miRNA) and gene expression profiles via machine learning (ML) approach to identify significant miRNA groups and their associated target genes. In this study, we propose miRcorrNetPro tool, which extends miRcorrNet by tracking group scoring, ranking and other information through the cross-validation iterations. Heatmap visualizations enable deep novel insights into the collective behavior of clusters of groups in cellular signaling and hence facilitate detection of potential biomarkers for the disease under investigation. Although miRcorrNetPro is designed as a generic tool, here we present our findings and potential miRNA biomarkers for Breast Cancer (BRCA). The miRcorrNetPro tool and all other supplementary files are available at https://github.com/Miray-Unlu/miRcorrNetPro. © 2024 Elsevier B.V., All rights reserved.
  • Conference Object
    The Effect of Different Classifiers on Recursive Cluster Elimination in the Analysis of Transcriptomic Data
    (Institute of Electrical and Electronics Engineers Inc., 2023-10-11) Bulut, Nurten; Bakir-Güngör, Burcu; Qaqish, Bahjat F.; Yousef, Malik
    Gene expression data with limited sample size and a large number of genes are frequently encountered in genetic studies. In such high-dimensional data, identification of genes that distinguish between disease states is a challenging task. Feature selection (FS) is a useful approach in dealing with high dimensionality. Support Vector Machines Recursive Cluster Elimination (SVM-RCE) is a technique for FS in high-dimensional data. The SVM-RCE approach has been utilized for identification of clusters of genes whose expression levels correlate with pathological state. A key step in SVM-RCE is the use of an SVM classifier to assign an area under the curve (AUC) score to each gene cluster based on its ability to predict class labels. In this study, we investigate the use of alternative classifiers in the cluster-scoring step. Specifically, we compare Support Vector Machines, Random Forest, XgBoost, Naive Bayes, and linear logistic regression. In addition to AUC score performance evaluation, the algorithms are compared in terms of the number of selected genes at different levels of clustering and in terms of the running time. © 2023 Elsevier B.V., All rights reserved.
  • Article
    Citation - Scopus: 25
    Recursive Cluster Elimination Based Rank Function (SVM-RCE-R) Implemented in KNIME
    (F1000 Research Ltd, 2021-01-05) Yousef, Malik; Bakir-Güngör, Burcu; Jabeer, Amhar; Göy, Gökhan; Qureshi, Rehman A.; C Showe, Louise; C. Showe, Louise
    In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify MicroRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. © 2021 Elsevier B.V., All rights reserved.
  • Conference Object
    Citation - Scopus: 2
    Effect of Recursive Cluster Elimination With Different Clustering Algorithms Applied to Gene Expression Data
    (Institute of Electrical and Electronics Engineers Inc., 2023-10-11) Kuzudisli, Cihan; Bakir-Güngör, Burcu; Qaqish, Bahjat F.; Yousef, Malik
    Feature selection (FS) is an effective tool in dealing with high dimensionality and reducing computational cost. Support Vector Machines-Recursive Cluster Elimination (SVM-RCE) is one of several algorithms that have been developed for FS in high dimensional data. SVM-RCE involves a clustering step which originally is k-means. Using various performance metrics, three alternative algorithms are evaluated in this context; k-medoids, Hierarchical Clustering (HC), and Gaussian Mixture Model (GMM). Comparisons will be carried out on five publicly available gene expression datasets. The results show that k-means in SVM-RCE obtains higher performance than other tested algorithms in terms of classification performance. Additionally, HC shows a similar performance to k-means. Our findings show superiority of using k-means. This study can contribute to the development of SVM-RCE with different variations, leading to decrease in the number of selected genes, and an increase in prediction performance. © 2023 Elsevier B.V., All rights reserved.
  • Conference Object
    Hepatoselüler Karsinom Oluşumunda Etkili Moleküler Mekanizmaların İn Siliko Yöntemlerle Araştırılması
    (Institute of Electrical and Electronics Engineers Inc., 2020-09) Doǧan, Refika Sultan; Saka, Samed; Bakir-Güngör, Burcu; Gungor, Burcu Bakir
    Hepatocellular carcinoma (HCC) is the most common cause of cancer-related death in the world. The molecular changes in the organism during the development of HCC are not fully understood. The aim of the present study is to contribute to the identification of critical genes and pathways associated with HCC via integrating various bioinformatics methods. In this study, experiments were conducted on gene expression data of 14 HCC tissues and noncancerous control tissues. A total of 1229 genes, which show a statistically significant change between the groups, were identified. Among these, 681 genes were upregulated and 548 genes were downregulated. Via mapping the detected genes into protein protein interaction networks, active subnetwork search, subnetwork topological analysis and functional enrichment analyses were carried out. The interactions between the molecular pathways affected by HCC were also presented. © 2020 Elsevier B.V., All rights reserved.
  • Conference Object
    Nöromüsküler Hastalıkların Ortak MikroRNA ve Yolaklarının İn Siliko Yöntemlerle Belirlenmesi
    (Institute of Electrical and Electronics Engineers Inc., 2019-04) Ünlü Yazici, Miray; Aksu-Menges, Evrim; Akkaya-Ulum, Yeliz Z.; Balcihayta, Burcu; Bakir-Güngör, Burcu
    Neuromuscular disorders (NMD) are a heterogeneous group of diseases characterized by the loss of function of the peripheral nerves and muscles. However, there are no effective and widespread therapeutic approaches to prevent or delay the progression of these disease types. MicroRNAs (miRNAs) which cause significant changes in gene expression by binding to target messenger RNAs (mRNAs), are known to have an effect on disease mechanisms. In this study, by integrating different bioinformatics methods, we aim to find miRNAs, target genes and pathways related to a group of neuromuscular diseases. For this purpose, we determined 17 miRNAs that show significant expression changes between patient and healthy groups; predicted target genes of these miRNAs; and identified affected pathways using subnetwork discovery, functional enrichment based algorithms. In our study, we integrated different in-silico approaches that proceed in topdown manner or bottom-up manner. The identified candidate miRNAs, genes and pathways, which could help to explain neuromuscular disease development mechanisms, are now under investigation in wet-lab. © 2020 Elsevier B.V., All rights reserved.