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DOĞAN, BERKCAN

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DOĞAN

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BERKCAN

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2019 - 201912020 - 20211
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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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    Hsa-miR-584-5p as a novel candidate biomarker in Turkish men with severe coronary artery disease
    (Springer, 2019-12-21) Çoban, Neslihan; Pirim, Dilek; Erkan, Ayçan Fahri; Doğan, Berkcan; Ekici, Berkay; PİRİM, DİLEK; DOĞAN, BERKCAN; Bursa Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Moleküler Biyoloji ve Genetik Bölümü; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Tıbbi Genetik Anabilim Dalı; 0000-0002-0522-9432; 0000-0001-8061-8131; HTP-6233-2023; AAD-5249-2020; ABA-4957-2020
    Coronary artery disease (CAD) is still the preliminary cause of mortality and morbidity in the developed world. Identification of novel predictive and therapeutic biomarkers is crucial for accurate diagnosis, prognosis and treatment of the CAD. The aim of this study was to detect novel candidate miRNA biomarker that may be used in the management of CAD. We performed miRNA profiling in whole blood samples of angiographically confirmed Turkish men with CAD and non-CAD controls with insignificant coronary stenosis. Validation of microarray results was performed by qRT-PCR in a larger cohort of 62 samples. We subsequently assessed the diagnostic value of the miRNA and correlations of miRNA with clinical parameters. miRNA-target identification and network analyses were conducted by Ingenuity Pathway Analysis (IPA) software. Hsa-miR-584-5p was one of the top significantly dysregulated miRNA observed in miRNA microarray. Men-specific down-regulation (p = 0.040) of hsa-miR-584-5p was confirmed by qRT-PCR. ROC curve analysis highlighted the potential diagnostic value of hsa-miR-584-5p with a power area under the curve (AUC) of 0.714 and 0.643 in men and in total sample, respectively. The expression levels of hsa-miR-584-5p showed inverse correlation with stenosis and Gensini scores. IPA revealed CDH13 as the only CAD related predicted target for the miRNA with biological evidence of its involvement in CAD. This study suggests that hsa-miR-584-5p, known to be tumor suppressor miRNA, as a candidate biomarker for CAD and highlighted its putative role in the CAD pathogenesis. The validation of results in larger samples incorporating functional studies warrant further research.