A Computational Approach to Discriminate AMD/ non-AMD Patients Based on Retina Tissue Gene Expression Profile: Machine Learning approaches to discriminate AMD/non-AMD samples - Amirvhaedi, Fazel; Maghsoudloo, Mazaher; Adhami-Moghadam, Farhad (Journal of Ophthalmic and Optometric Sciences, 2021)
Background: Age-related macular degeneration (AMD) is the progressive degenerative disease of the macula and the main cause of blindness in older adults. Various risk factors have been associated with disease progression among different individuals.AMD is affected by different risk factors such as aging, genetic susceptibility, environmental risk factors and lifestyle. Since the etiology of AMD is not fully known, it would be essential to identify disease risk factors and novel predictive risk factors to detect AMD at an early stage. Material and Methods: The expression data were obtained from the Gene Expression Omnibus database. Samples were quantile normalized, and log2 transformed. Furthermore, outlier samples were removed by hierarchical clustering. R limma was used to run a linear model and identify differentially expressed genes (DEGs). As a result, 33 genes were discovered with a q-value less than 0.05 and a |log (FC)|≥0.7. With a machine learning (ML) approach, DEGs were applied to discriminate between the case and control samples. Furthermore, FeatureSelect is used to extract the most effective separator genes. Nine genes were identified as the best disease discriminator genes through 11 feature selection algorithms. Results: The gene set found in the study distinguishes healthy samples from patient samples with an accuracy of 87.5 %. We found DEF119B, UBD, and GRP to be three novel potential AMD candidate biomarkers using ML models and feature selection. Conclusion: Machine learning can be beneficial in diagnosing, preventing and treating diseases, especially in diseases such as AMD that do not have a clear etiology.