Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 6Citation - Scopus: 7The Determination of Distinctive Single Nucleotide Polymorphism Sets for the Diagnosis of Behcet's Disease(IEEE Computer Soc, 2022-05-01) Isik, Yunus Emre; Gormez, Yasin; Aydin, Zafer; Bakir-Gungor, BurcuBehcet's Disease (BD) is a multi-system inflammatory disorder in which the etiology remains unclear. The most probable hypothesis is that genetic tendency and environmental factors play roles in the development of BD. In order to find the essential reasons, genetic changes on thousands of genes should be analyzed. Besides, there is a need for extra analysis to find out which genetic factor affects the disease. Machine learning approaches have high potential for extracting the knowledge from genomics and selecting the representative Single Nucleotide Polymorphisms (SNPs) as the most effective features for the clinical diagnosis process. In this study, we have attempted to identify representative SNPs using feature selection methods, incorporating biological information and aimed to develop a machine-learning model for diagnosing Behcet's disease. By combining biological information and machine learning classifiers, up to 99.64 percent accuracy of disease prediction is achieved using only 13,611 out of 311,459 SNPs. In addition, we revealed the SNPs that are most distinctive by performing repeated feature selection in cross-validation experiments.Article Citation - WoS: 5Citation - Scopus: 5Novel Antimicrobial Peptide Design Using Motif Match Score Representation(IEEE Computer Soc, 2024-11) Soylemez, Ummu Gulsum; Yousef, Malik; Kesmen, Zulal; Bakir-Gungor, BurcuAntimicrobial peptides (AMPs) have drawn the interest of the researchers since they offer an alternative to the traditional antibiotics in the fight against antibiotic resistance and they exhibit additional pharmaceutically significant properties. Recently, computational approaches attemp to reveal how antibacterial activity is determined from a machine learning perspective and they aim to search and find the biological cues or characteristics that control antimicrobial activity via incorporating motif match scores. This study is dedicated to the development of a machine learning framework aimed at devising novel antimicrobial peptide (AMP) sequences potentially effective against Gram-positive/Gram-negative bacteria. In order to design newly generated sequences classified as either AMP or non-AMP, various classification models were trained. These novel sequences underwent validation utilizing the "DBAASP: strain-specific antibacterial prediction based on machine learning approaches and data on AMP sequences" tool. The findings presented herein represent a significant stride in this computational research, streamlining the process of AMP creation or modification within wet lab environments.