Browsing by Author "Söylemez, Ümmü Gülsüm"

Now showing 1 - 2 of 2

Citation - Scopus: 1
Correlation of PAPP-A Values With Maternal Characteristics, Biochemical and Ultrasonographic Markers of Pregnancy
(Marmara Univ, Fac Medicine, 2021) Kaymakcalan, Hande; Uzut, Ommu Gulsum; Harkonen, Juho; Bakir Gungor, Burcu; Bakir-gungor, Burcu; Söylemez, Ümmü Gülsüm
Objective: Our aim is to investigate whether there is a correlation of pregnancy-associated plasma protein A (PAPP-A) values with other variables in pregnancy and maternal characteristics. Materials and Methods: We retrospectively analyzed the relation between the PAPP-A levels, demographics, biochemical and ultrasonographic markers of the first trimester screening of 11,842 pregnant women seen at a tertiary hospital between November 2002 and November 2008. Results: A significant difference between PAPP-A values of the diabetic and non-diabetic pregnant women were observed (p=0.0005, Mann-Whitney U test). In terms of weight, crown-rump length, Beta-hCG values, significant differences were observed between low and medium level PAPP-A subgroups and between low and high level PAPP-A subgroups. PAPP-A levels were found to differ significantly between the pregnant women of Caucasian origin and other racial origins. Conclusions: Pregnant women with different ethnic and medical backgrounds have different PAPP-A values and other markers of the aneuploidy screening. 'lb make patient specific risk predictions, understanding these interactions and differences is important. Future studies are needed to understand the pathopyhsiology behind these differences.
Makine Öğrenmesi Yöntemleriyle Antimikrobiyal Peptit Aktivite Tahmini
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Söylemez, Ümmü Gülsüm; Güngör, Burcu
Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this thesis, using the multiple properties of the peptides we aimed to develop machine learning approaches that can predict the antimicrobial activities of the peptides. We have created two datasets for the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately. In our first study, ten different physico-chemical properties of the peptides were calculated, and used as features of the peptides. Following the data exploration and data preprocessing steps, a variety of classification models were build with 100-fold Monte Carlo Cross-Validation; and the performance of these models were evaluated. In the second study, we proposed a novel method called AMP-GSM. The method was tested for three datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. In the last study, using motif matching score with the models of activity against Gram-positive and Gram-negative bacteria, we created novel peptides and predicted the target selectivity of these peptides. The studies presented in this thesis advance the field of computational research by making it easier to predict the possible antimicrobial effects of peptides and to design AMPs in wet laboratory environments.