Publications by authors named "D Goksuluk"
Article Synopsis
- - RNA sequencing (RNA-Seq) has improved our understanding of gene expression in various conditions, but analyzing the large and complex data from RNA-Seq to identify differentially expressed genes (DEGs) is challenging, especially for diseases like cancer.
- - The study introduces a new method called Machine Learning-Enhanced Genomic Data Analysis Pipeline (ML-GAP), which uses autoencoders and a technique called MixUp to generate synthetic training examples, improving the model's ability to recognize patterns in unseen data.
- - Results indicate that ML-GAP outperforms traditional methods in accuracy and efficiency for detecting DEGs, suggesting potential benefits for disease treatment and research, while also ensuring the analysis remains transparent through explainable AI techniques.
Objectives: Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual's dental age or defining an age classification. This study aims to use ML algorithms to evaluate the efficacy of pulp/tooth area ratio (PTR) in cone-beam CT (CBCT) images to predict dental age classification in adults.
Methods: CBCT images of 236 Turkish individuals (121 males and 115 females) from 18 to 70 years of age were included.
Purpose: The purpose of the study is to evaluate the low vision rehabilitation methods and to investigate the effect of visual rehabilitation on quality of life in patients with low vision due to geographic atrophy from age-related macular degeneration (ARMD).
Methods: The better-seeing eye of 78 patients with geographic atrophy due to ARMD were included in the study. Sociodemographic characteristics, ophthalmological examination findings, and preferred low vision aids for near and distant were recorded.
Background: Coronary artery disease is a complex disorder that causes death worldwide. One of the genes involved in developing this disease may be PTEN.
Objectives: This study aimed to investigate the PTEN gene and protein expression in tissue and blood samples taken from coronary bypass surgery patients.
Healthcare workers (HCWs) also became the main protagonist of the tragic pandemic story. They have had a markedly higher risk of becoming infected with COVID-19. Outside work, healthcare workers with children have experienced mental health challenges, including the worry that they may carry COVID-19 home and infect their children.
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