Experimental eye research · 2026
OMICS data in the diagnosis of diabetic retinopathy: A comparison between transcriptome data and DNA methylation data.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Jeyananthan Pratheeba, Chandrasiri H V B L, Ashfa A G F
Аннотация
PURPOSE: Diabetic retinopathy is a serious complication of diabetes that can damage the retina of the eye. It potentially leads to vision impairment or blindness if left untreated. Even though it can be diagnosed using dilated eye examinations, advancements in current technologies have enabled us towards more accurate diagnosis using diverse digital data sources such as medical images and omics data. However, medical images were widely used in the diagnosis of this disease using machine learning algorithms. Analysing omics data offers several advantages over image-based approaches, such as lower complexity, reduced time and less computational requirements. Moreover, omics data analysis facilitates the identification of biomarkers, which can reveal some valuable insights regarding the underlying biological mechanism of the disease.
DESIGN: Hence, this study used three different omics data, such as DNA methylation, total RNA and small RNA in the diagnosis of diabetic retinopathy using different machine learning algorithms.
METHODS: Four different feature selection algorithms were individually used with each data to select the biomarkers of the study, and the best set of features was used with different machine learning algorithms to identify the model with the highest accuracy.
RESULTS: Comparing the accuracies between models showed that 14 total RNA features selected using feature importance, along with naïve Bayes algorithms, outperformed other models with an accuracy value of 0.9625 ± 0.05.
CONCLUSIONS: Naïve Bayes algorithm using total RNA data can achieve significant performance in retinopathy diagnosis.
DESIGN: Hence, this study used three different omics data, such as DNA methylation, total RNA and small RNA in the diagnosis of diabetic retinopathy using different machine learning algorithms.
METHODS: Four different feature selection algorithms were individually used with each data to select the biomarkers of the study, and the best set of features was used with different machine learning algorithms to identify the model with the highest accuracy.
RESULTS: Comparing the accuracies between models showed that 14 total RNA features selected using feature importance, along with naïve Bayes algorithms, outperformed other models with an accuracy value of 0.9625 ± 0.05.
CONCLUSIONS: Naïve Bayes algorithm using total RNA data can achieve significant performance in retinopathy diagnosis.