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ALIYEVA, LAMIYA

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ALIYEVA

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LAMIYA

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2020 - 20223
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    Publication
    BRCA variations risk assessment in breast cancers using different artificial intelligence models
    (Mdpi, 2021-11-08) Şentürk, Niyazi; Tuncel, Gülten; Doğan, Berkcan; Aliyeva, Lamiya; Dündar, Mehmet Sait; Özemri Sağ, Şebnem; Mocan, Gamze; Temel, Şehime Gülsün; Dündar, Munis; Ergoren, Mahmut Çerkez; DOĞAN, BERKCAN; ALIYEVA, LAMIYA; ÖZEMRİ SAĞ, ŞEBNEM; TEMEL, ŞEHİME GÜLSÜN; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı.; Bursa Uludağ Üniversitesi/Sağlık Bilimleri Enstitüsü/Translasyonel Tıp Anabilim Dalı.; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Histoloji ve Embriyoloji Anabilim Dalı.; 0000-0001-8061-8131; CCG-4609-2022 ; AAH-8355-2021 ; AAD-5249-2020; AAG-8385-2021
    Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software.
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    Publication
    Diagnostic efficiency of clinical exome solution panel in patients with hearing loss/hereditary deafness by using next generation sequencing
    (Springernature, 2020-12-01) ; Sağ, Şebnem Özemri; ÖZEMRİ SAĞ, ŞEBNEM; Alemdar, A.; ALEMDAR, ADEM; Yılmaz, M.; Aliyeva, L.; ALIYEVA, LAMIYA; Temel Şehime Gülsün; TEMEL, ŞEHİME GÜLSÜN; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Genetik Top Anabilim Dalı.; 0000-0002-9802-0880; HIZ-7332-2022; AAH-8355-2021; AAG-8385-2021
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    Publication
    Clinical and molecular evaluation of MEFV gene variants in the Turkish population: a study by the National Genetics Consortium
    (Springer Heidelberg, 2022-01-31) Dundar, Munis; Fahrioglu, Umut; Yildiz, Saliha Handan; Bakir-Gungor, Burcu; Temel, Sehime Gulsun; Akin, Haluk; Artan, Sevilhan; Cora, Tulin; Sahin, Feride Iffet; Dursun, Ahmet; Sezer, Ozlem; Gurkan, Hakan; Erdogan, Murat; Gunduz, C. Nur Semerci; Bisgin, Atil; Ozdemir, Ozturk; Ulgenalp, Ayfer; Percin, E. Ferda; Yildirim, Malik Ejder; Tekes, Selahaddin; Bagis, Haydar; Yuce, Huseyin; Duman, Nilgun; Bozkurt, Gokay; Yararbas, Kanay; Yildirim, Mahmut Selman; Arman, Ahmet; Mihci, Ercan; Eraslan, Serpil; Altintas, Zuhal Mert; Aymelek, Huri Sema; Ruhi, Hatice Ilgin; Tatar, Abdulgani; Ergoren, Mahmut Cerkez; Cetin, G. Ozan; Altunoglu, Umut; Caglayan, Ahmet Okay; Yuksel, Berrin; Ozkul, Yusuf; Saatci, Cetin; Kenanoglu, Sercan; Karasu, Nilgun; Dundar, Bilge; Ozcelik, Firat; Demir, Mikail; Siniksaran, Betul Seyhan; Kulak, Hande; Kiranatlioglu, Kubra; Baysal, Kubra; Kazimli, Ulviyya; Akalin, Hilal; Dundar, Ayca; Boz, Mehmet; Bayram, Arslan; Subasioglu, Asli; Colak, Fatma Kurt; Karaduman, Neslihan; Gunes, Meltem Cerrah; Kandemir, Nefise; Aynekin, Busra; Emekli, Rabia; Sahin, Izem Olcay; Ozdemir, Sevda Yesim; Onal, Muge Gulcihan; Senel, Abdurrahman Soner; Poyrazoglu, Muammer Hakan; Kisaarslan, Ayse Nur Pac; Gursoy, Sebnem; Baskol, Mevlut; Calis, Mustafa; Demir, Huseyin; Zararsiz, Gozde Erturk; Erdogan, Mujgan Ozdemir; Elmas, Muhsin; Solak, Mustafa; Ulu, Memnune Sena; Thahir, Adam; Aydin, Zafer; Atasever, Umut; Sag, Sebnem Ozemri; Aliyeva, Lamiya; Alemdar, Adem; Dogan, Berkcan; Erguzeloglu, Cemre Ornek; Kaya, Niyazi; Ozkinay, Ferda; Cogulu, Ozgur; Durmaz, Asude; Onay, Huseyin; Karaca, Emin; Durmaz, Burak; Aykut, Ayca; Cilingir, Oguz; Aras, Beyhan Durak; Gokalp, Ebru Erzurumluoglu; Arslan, Serap; Temena, Arda; Haziyeva, Konul; Kocagil, Sinem; Bas, Hasan; Susam, Ezgi; Keklikci, Ali Riza; Sarac, Elif; Kocak, Nadir; Nergiz, Suleyman; Terzi, Yunus Kasim; Dincer, Selin Akad; Baskin, Esra Sidika; Genc, Gunes Cakmak; Bahadir, Oguzhan; Sanri, Aslihan; Yigit, Serbulent; Tozkir, Hilmi; Yalcintepe, Sinem; Ozkayin, Nese; Kiraz, Aslihan; Balta, Burhan; Gonen, Gizem Akinci; Kurt, E. Emre; Ceylan, Gulay Gulec; Ceylan, Ahmet Cevdet; Erten, Sukran; Bozdogan, Sevcan Tug; Boga, Ibrahim; Yilmaz, Mustafa; Silan, Fatma; Kocabey, Mehmet; Koc, Altug; Cankaya, Tufan; Bora, Elcin; Bozkaya, Ozlem Giray; Ercal, Derya; Ergun, Mehmet Ali; Ergun, Sezen Guntekin; Duman, Yesim Sidar; Beyazit, Serife Busra; Uzel, Veysiye Hulya; Em, Serda; Cevik, Muhammer Ozgur; Eroz, Recep; Demirtas, Mercan; Firat, Cem Koray; Kabayegit, Zehra Manav; Altan, Mustafa; Mardan, Lamiya; Sayar, Ceyhan; Tumer, Sait; Turkgenc, Burcu; Karakoyun, Hilal Keskin; Tunc, Betul; Kuru, Seda; Zamani, Aysegul; Geckinli, Bilgen Bilge; Ates, Esra Arslan; Clark, Ozden Altiok; Toylu, Asli; Coskun, Mert; Nur, Banu; Bilge, Ilmay; Bayramicli, Oya Uygur; Emmungil, Hakan; Komesli, Zeynep; Zeybel, Mujdat; Gurakan, Figen; Tasdemir, Mehmet; Kebudi, Rejin; Karabulut, Halil Gurhan; Tuncali, Timur; Kutlay, Nuket Yurur; Kahraman, Cigdem Yuce; Onder, Nerin Bahceciler; Beyitler, Ilke; Kavukcu, Salih; Tulay, Pinar; Tosun, Ozgur; Tuncel, Gulten; Mocan, Gamze; Kale, Hamdi; Uyguner, Zehra Oya; Acar, Aynur; Altinay, Mert; Erdem, Levent; TEMEL, ŞEHİME GÜLSÜN; ÖZEMRİ SAĞ, ŞEBNEM; ALIYEVA, LAMIYA; ALEMDAR, ADEM; DOĞAN, BERKCAN; Ergüzeloğlu, Cemre Örnek; Kaya, Niyazi; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı.; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Histoloji ve Embriyoloji Anabilim Dalı.; Bursa Uludağ Üniversitesi/Sağlık Bilimleri Enstitüsü/Translasyonel Tıp Anabilim Dalı.; 0000-0001-8061-8131; AAG-8385-2021; AAH-8355-2021; CCG-4609-2022; HIZ-7332-2022; AAD-5249-2020; EXQ-7887-2022; FEL-0562-2022
    Familial Mediterranean fever (FMF) is a monogenic autoinflammatory disorder with recurrent fever, abdominal pain, serositis, articular manifestations, erysipelas-like erythema, and renal complications as its main features. Caused by the mutations in the MEditerranean FeVer (MEFV) gene, it mainly affects people of Mediterranean descent with a higher incidence in the Turkish, Jewish, Arabic, and Armenian populations. As our understanding of FMF improves, it becomes clearer that we are facing with a more complex picture of FMF with respect to its pathogenesis, penetrance, variant type (gain-of-function vs. loss-of-function), and inheritance. In this study, MEFV gene analysis results and clinical findings of 27,504 patients from 35 universities and institutions in Turkey and Northern Cyprus are combined in an effort to provide a better insight into the genotype-phenotype correlation and how a specific variant contributes to certain clinical findings in FMF patients. Our results may help better understand this complex disease and how the genotype may sometimes contribute to phenotype. Unlike many studies in the literature, our study investigated a broader symptomatic spectrum and the relationship between the genotype and phenotype data. In this sense, we aimed to guide all clinicians and academicians who work in this field to better establish a comprehensive data set for the patients. One of the biggest messages of our study is that lack of uniformity in some clinical and demographic data of participants may become an obstacle in approaching FMF patients and understanding this complex disease.