Zobrazeno 1 - 10
of 42
pro vyhledávání: '"O Uğur Sezerman"'
Autor:
Timucin Avsar, İlknur Melis Durası, Uğur Uygunoğlu, Melih Tütüncü, Nuri Onat Demirci, Sabahattin Saip, O Uğur Sezerman, Aksel Siva, Eda Tahir Turanlı
Publikováno v:
PLoS ONE, Vol 10, Iss 5, p e0122045 (2015)
Multiple sclerosis (MS) is an immune-mediated, neuro-inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS) with a heterogeneous clinical presentation and course. There is a remarkable phenotypic heterogeneity i
Externí odkaz:
https://doaj.org/article/06e768ed585c4ccdb304110374274f75
Autor:
Ege Ülgen, O. Uğur Sezerman
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-17 (2021)
Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study
Externí odkaz:
https://doaj.org/article/c67ed18962014c568dbc1348bd00006c
Autor:
Ege Ülgen, Özge Can, Kaya Bilguvar, Cemaliye Akyerli Boylu, Şirin Kılıçturgay Yüksel, Ayça Erşen Danyeli, O. Uğur Sezerman, M. Cengiz Yakıcıer, M. Necmettin Pamir, Koray Özduman
Publikováno v:
BMC Medical Genomics, Vol 14, Iss 1, Pp 1-14 (2021)
Abstract Background In the clinical setting, workflows for analyzing individual genomics data should be both comprehensive and convenient for clinical interpretation. In an effort for comprehensiveness and practicality, we attempted to create a clini
Externí odkaz:
https://doaj.org/article/8d68f504ee3442278efa6a1282e8f27e
Autor:
Ege Ülgen, Sıla Karacan, Umut Gerlevik, Özge Can, Kaya Bilguvar, Yavuz Oktay, Cemaliye B. Akyerli, Şirin K. Yüksel, Ayça E. Danyeli, Tarık Tihan, O. Uğur Sezerman, M. Cengiz Yakıcıer, M. Necmettin Pamir, Koray Özduman
Publikováno v:
Biomedicines, Vol 8, Iss 12, p 574 (2020)
Little is known about the mutational processes that shape the genetic landscape of gliomas. Numerous mutational processes leave marks on the genome in the form of mutations, copy number alterations, rearrangements or their combinations. To explore gl
Externí odkaz:
https://doaj.org/article/7243f9e585904f9493bea5e128696565
Currently, potential vaccine candidates are determined using protein localization predictors to identify surface or secreted protein sequences. The pathogenic bacterial sequences constitute only a tiny percentage of this database. They search for gen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b58c1909d8342a6e147e779312ef9731
https://doi.org/10.1101/2022.06.10.495625
https://doi.org/10.1101/2022.06.10.495625
Autor:
M. Cengiz Yakıcıer, O. Uğur Sezerman, Kaya Bilguvar, Ayça Erşen Danyeli, Cemaliye B Akyerli, M. Necmettin Pamir, Özge Can, Yavuz Oktay, Ege Ülgen, Koray Özduman
Publikováno v:
Journal of Neurosurgery. 132:1435-1446
OBJECTIVEProcesses that cause or contribute to cancer, such as aging, exposure to carcinogens, or DNA damage repair deficiency (DDRd), create predictable and traceable nucleotide alterations in one’s genetic code (termed “mutational signatures”
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fba73a9f1331b5afbf26c382f0940125
https://doi.org/10.3171/2019.1.jns182938
https://doi.org/10.3171/2019.1.jns182938
Autor:
Ülgen, Ege, Can, Özge, Bilguvar, Kaya, Cemaliye Akyerli Boylu, Şirin Kılıçturgay Yüksel, Danyeli, Ayça Erşen, O. Uğur Sezerman, M. Cengiz Yakıcıer, M. Necmettin Pamir, Özduman, Koray
Additional file 2. Supplementary figures and tables.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fab3bc65bcc4e4d08b00329eb259361
Autor:
Ülgen, Ege, O. Uğur Sezerman
Additional file 2: Figure S2. Performance of different coding variant impact metapredictor models in the training (A) and test (B) datasets.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ca945c3826d55f1f923a42de2edf59c
Autor:
Ülgen, Ege, O. Uğur Sezerman
Additional file 5: Table S1. Datasets used for training and testing the multi-task learning classification model. “Type” indicates whether the dataset was used for training or testing. “Dataset ID” indicates the name of the dataset. “Cancer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0a96940c0435d7e0732f3be41841391
Autor:
Ülgen, Ege, O. Uğur Sezerman
Additional file 4: Figure S4. Comparison of performance of driveR with personalized analysis approaches per test dataset. Boxplots displaying the distributions of AUC values of each approach across all patients per test dataset. The bracket display p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3edc00828a30b7dea107b72a98d368c5