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Permanent URI for this collectionhttps://hdl.handle.net/11443/932

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Author: search.filters.author.Ulgen, EgeHas files: No

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    Investigation of multiple sclerosis-related pathways through the integration of genomic and proteomic data
    (PEERJ INC, 2021-01-01) Everest, Elif; Ulgen, Ege; Uygunoglu, Ugur; Tutuncu, Melih; Saip, Sabahattin; Sezerman, Osman Ugur; Siva, Aksel; Turanli, Eda Tahir
    Background. Multiple sclerosis (MS) has a complex pathophysiology, variable clinical presentation, and unpredictable prognosis
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    CogNet: classification of gene expression data based on ranked active-subnetwork- oriented KEGG pathway enrichment analysis
    (PEERJ INC, 2021-01-01) Yousef, Malik; Ulgen, Ege; Sezerman, Osman Ugur
    Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological resources. In this study, we propose a new computational approach named CogNet that is an integrative gene selection tool that exploits biological knowledge for grouping the genes for the computational modeling tasks of ranking and classification. In CogNet, the pathfindR serves as the biological grouping tool to allow the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis results to build a biologically relevant model. CogNet provides a list of significant KEGG pathways that can classify the data with a very high accuracy. The list also provides the genes belonging to these pathways that are differentially expressed that are used as features in the classification problem. The list facilitates deep analysis and better interpretability of the role of KEGG pathways in classification of the data thus better establishing the biological relevance of these differentially expressed genes. Even though the main aim of our study is not to improve the accuracy of any existing tool, the performance of the CogNet outperforms a similar approach called maTE while obtaining similar performance compared to other similar tools including SVM-RCE. CogNet was tested on 13 gene expression datasets concerning a variety of diseases.
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    Correlation of anatomical involvement patterns of insular gliomas with subnetworks of the limbic system
    (AMER ASSOC NEUROLOGICAL SURGEONS, 2022-01-01) Ulgen, Ege; Aras, Fuat Kaan; Cosgun, Erdal; Ersen-Danyeli, Ayca; Dincer, Alp; Usseli, M. Imre; Ozduman, Koray; Pamir, M. Necmettin
    OBJECTIVE Gliomas frequently involve the insula both primarily and secondarily by invasion. Despite the high connectivity of the human insula, gliomas do not spread randomly to or from the insula but follow stereotypical anatomical involvement patterns. In the majority of cases, these patterns correspond to the intrinsic connectivity of the limbic system, except for tumors with aggressive biology. On the basis of these observations, the authors hypothesized that these different involvement patterns may be correlated with distinct outcomes and analyzed these correlations in an institutional cohort. METHODS Fifty-nine patients who had undergone surgery for insular diffuse gliomas and had complete demographic, pre- and postoperative imaging, pathology, molecular genetics, and clinical follow-up data were included in the analysis (median age 37 years, range 21-71 years, M/F ratio 1.68). Patients with gliomatosis and those with only minor involvement of the insula were excluded. The presence of T2-hyperintense tumor infiltration was evaluated in 12 anatomical structures. Hierarchical biclustering was used to identify co-involved structures, and the findings were correlated with established functional anatomy knowledge. Overall survival was evaluated using Kaplan-Meier and Cox proportional hazards regression analysis (17 parameters). RESULTS The tumors involved the anterior insula (98.3\%), posterior insula (67.8\%), temporal operculum (47.5\%), amygdala (42.4\%), frontal operculum (40.7\%), temporal pole (39\%), parolfactory area (35.6\%), hypothalamus (23.7\%), hippocampus (16.9\%), thalamus (6.8\%), striatum (5.1\%), and cingulate gyrus (3.4\%). A mean 4.2 +/- 2.6 structures were involved. On the basis of hierarchical biclustering, 7 involvement patterns were identified and correlated with cortical functional anatomy (pure insular {[}11.9\%], olfactocentric {[}15.3\%], olfactoopercular {[}33.9\%], operculoinsular {[}15.3\%], striatoinsular {[}3.4\%], translimbic {[}11.9\%], and multifocal {[}8.5\%] patterns). Cox regression identified hippocampal involvement (p = 0.006) and postoperative tumor volume (p = 0.027) as significant negative independent prognosticators of overall survival and extent of resection (p = 0.015) as a significant positive independent prognosticator. CONCLUSIONS The study findings indicate that insular gliomas primarily involve the olfactocentric limbic girdle and that involvement in the hippocampocentric limbic girdle is associated with a worse prognosis.