Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Impact of Gene Duplicate Handling Strategies on Classification Performance and Feature Selection in Gene Expression Data(Institute of Electrical and Electronics Engineers Inc., 2025-09-17) Kuzudisli, Cihan; Qaqish, Bahjat; Gungor, Burcu Bakir; Yousef, MalikArticle Citation - WoS: 7Citation - Scopus: 7In Silico Analyses and Global Transcriptional Profiling Reveal Novel Putative Targets for Pea3 Transcription Factor Related to Its Function in Neurons(Public Library Science, 2017-02-03) Kandemir, Basak; Dag, Ugur; Gungor, Burcu Bakir; Durasi, Ilknur Melis; Erdogan, Burcu; Sahin, Eray; Kurnaz, Isil AksanPea3 transcription factor belongs to the PEA3 subfamily within the ETS domain transcription factor superfamily, and has been largely studied in relation to its role in breast cancer metastasis. Nonetheless, Pea3 plays a role not only in breast tumor, but also in other tissues with branching morphogenesis, including kidneys, blood vasculature, bronchi and the developing nervous system. Identification of Pea3 target promoters in these systems are important for a thorough understanding of how Pea3 functions. Present study particularly focuses on the identification of novel neuronal targets of Pea3 in a combinatorial approach, through curation, computational analysis and microarray studies in a neuronal model system, SH-SY5Y neuroblastoma cells. We not only show that quite a number of genes in cancer, immune system and cell cycle pathways, among many others, are either up- or down-regulated by Pea3, but also identify novel targets including ephrins and ephrin receptors, semaphorins, cell adhesion molecules, as well as metalloproteases such as kallikreins, to be among potential target promoters in neuronal systems. Our overall results indicate that rather than early stages of neurite extension and axonal guidance, Pea3 is more involved in target identification and synaptic maturation.Conference Object Enhancing Gene Expression Data Analysis Through SVM-Based Recursive Cluster Elimination and Weighted Center Approaches(Avestia Publishing, 2024-08) Yousef, Malik; Bulut, Nurten; Gungor, Burcu Bakir; Qaqish, Bahjat F.The complexity and high dimensionality of gene expression data pose significant challenges for effective feature selection and accurate classification in bioinformatics. This study introduces two novel algorithms, Support Vector Machine-Recursive Cluster Elimination (SVM-RCE) and its advanced version, SVM-RCE with Center Weights (SVM-RCE-CW), designed to optimize feature selection by leveraging clustering techniques and machine learning models. Both algorithms aim to reduce the feature space, thereby enhancing the interpretability and performance of classification models. We present a comprehensive comparison of these methods against traditional feature selection techniques, demonstrating their efficacy in achieving significant dimensionality reduction while maintaining or improving classification accuracy in several gene expression datasets. © 2024 Elsevier B.V., All rights reserved.Conference Object Comparison of Disease Specific Sub-Network Identification Programs(Institute of Electrical and Electronics Engineers Inc., 2018-09) Dedeturk, Beyhan Adanur; Bakir-Güngör, Burcu; Adanur, Beyhun; Gungor, Burcu BakirActive sub-network search aims to identify a group of interconnected genes in a protein-protein interaction network that contains most of the disease-associated genes. In recent years, to address active sub-network search problem, various algorithms and programs are developed. In this study, performances of disease specific sub-network identification programs are compared. The same input dataset is run in jActiveModules, ActiveSubnetworkGA, CytoHubba, ClusterViz, MCODE, CytoMOBAS, PathFindR, PINBPA and PEWCC programs. Then, functional enrichment analysis is applied on obtained sub-networks. Finally, they are compared according to the results of GO Enrichment Analysis. In addition to these, work performances, features and requirements of programs are compared. © 2019 Elsevier B.V., All rights reserved.