Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Predicting Respiratory Infection and Symptoms Development Using Gene Set Enrichment Scores and Machine Learning(Elsevier Sci Ltd, 2026) Aydin, Zafer; Isik, Yunus EmreRecent advancements in precision medicine enable personalized predictions grounded in individual-level genetic data. However, relying solely on a single type of data can decrease prediction accuracy and limit the biological interpretability of the resulting models. Incorporating predefined genetic knowledge, such as derived gene sets, can improve performance and provide deeper biological insights for complex diseases, including respiratory infections. This study aimed to evaluate the usability of enrichment scores (ES), calculated using gene sets from the Molecular Signatures Database (MSigDB), as a feature representation for machine learning models to predict respiratory viral infections and symptom development. In addition, the proposed feature representation approach was extensively compared with the de facto gene-level expression representation. A total of 36,834 predefined gene sets were compiled from the MSigDB, and their ES values were calculated. Experiments used the GSE73072 dataset from Gene Expression Omnibus, containing gene expression profiles before and after virus exposure. Various machine learning and feature selection algorithms were applied to ES-based and probe-level feature sets. The results showed that both feature representation approaches achieved an area under the precision-recall curve (AUPRC) value greater than 0.90 for all tasks. Compared with the Respiratory Viral DREAM Challenge leaderboard phase, our models showed a 14.8% improvement in pre-exposure predictions (T0) and a 17.4% improvement in symptom classification. Using enrichment scores as a feature representation generally resulted in better performance than probe-level representation when predicting respiratory infections and symptom development. Identifying key gene sets through feature selection and comparing them with essential genes for respiratory viruses enabled a more comprehensive analysis, providing deeper insights into the pathways that contribute to these predictions.Article Citation - WoS: 36Citation - Scopus: 36Histopathological and Biomechanical Evaluation of Tenocyte Seeded Allografts on Rat Achilles Tendon Regeneration(Elsevier Sci Ltd, 2015-05) Gungormus, Cansin; Kolankaya, Durdane; Aydin, ErkinTendon injuries in humans as well as in animals' veterinary medicine are problematic because tendon has poor regenerative capacity and complete regeneration of the ruptured tendon is never achieved. In the last decade there has been an increasing need of treatment methods with different approaches. The aim of the current study was to improve the regeneration process of rat Achilles tendon with tenocyte seeded decellularized tendon matrices. For this purpose, Achilles tendons were harvested, decellularized and seeded as a mixture of three consecutive passages of tenocytes at a density of 1 x 10(6) cells/ml. Specifically, cells with different passage numbers were compared with respect to growth characteristics, cellular senescence and collagen/tenocyte marker production before seeding process. The viability of reseeded tendon constructs was followed postoperatively up to 6 months in rat Achilles tendon by histopathological and biomechanical analysis. Our results suggests that tenocyte seeded decellularized tendon matrix can significantly improve the histological and biomechanical properties of tendon repair tissue without causing adverse immune reactions. To the best of our knowledge, this is the first long-term study in the literature which was accomplished to prove the use of decellularized matrix in a clinically relevant model of rat Achilles tendon and the method suggested herein might have important implications for translation into the clinic. (C) 2015 Elsevier Ltd. All rights reserved.