Publications by authors named "Y Akbulut"

Article Synopsis
  • The study investigates how machine learning, particularly traditional methods and graph neural networks (GNNs), can be used to predict diseases by analyzing complex protein-protein interactions (PPIs).
  • PPIs are challenging to analyze due to their complexity and variability, making traditional computational methods less effective.
  • Machine learning and GNNs are shown to be more effective in detecting complex patterns and achieving better accuracy in predictions across networks of different sizes, with an emphasis on the importance of network features.
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Therapeutic intervention targeting mRNA typically aims at reducing the levels of disease-causing sequences. Achieving the opposite effect of blocking the destruction of beneficial mRNA remains underexplored. The degradation of mRNA starts with the removal of poly(A) tails, reducing their stability and translational activity, which is mainly regulated by the CCR4-NOT complex.

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Background: Hip fractures are a serious public health problem with high rates of morbidity, mortality, disability and care costs. The aim of the research was to perform cost effectiveness analysis of hip fracture treatments using proximal femoral nail and bipolar hemiarthroplasty surgeries.

Methods: The analysis was completed based on the perspectives of the paying institution and patient.

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Background: Nutrient foramen (NF) is a hole on the long bones that allows the passage of the nutrient artery. The vasculature of the bone is very important for fracture healing and vascularized bone grafting. Therefore, information about the location and number of NFs is important for surgical and clinical practice.

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Screening for small-molecule modulators of disease-relevant targets and phenotypes is the first step on the way to new drugs. Large compound libraries have been synthesized by academia and, particularly, pharmaceutical companies to meet the need for novel chemical entities that are as diverse as possible. Screening of these compound libraries revealed a portion of small molecules that is inactive in more than 100 different assays and was therefore termed "dark chemical matter" (DCM).

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