Utilizing Artificial Intelligence and Single-Cell RNA-seq Data for the Investigation and Discovery of Novel Genetic Biomarkers in Age-Related Macular Degeneration
Session Number
Project ID: RISE 01
Advisor(s)
Mrs. Allison Hennings, Illinois Mathematics and Science Academy
Mr. Will DeGroat, Rutgers Institute of Health
Mr. Safdar Zaman, Microsoft
Dr. Linsey Mao Ph.D, Benedictine University
Discipline
Computer Science
Start Date
17-4-2024 9:20 AM
End Date
17-4-2024 9:35 AM
Abstract
Age-related macular degeneration (AMD) is a progressive neurodegenerative eye disorder characterized by eventual degeneration of the retinal pigment epithelium (RPE) leading to permanent vision loss. Artificial intelligence (AI) and machine learning (ML) have revolutionized healthcare by advancing clinical diagnosis leveraging its ability to analyze vast amounts of patient data and accurately predict future outcomes. With no definitive treatment for AMD, this experiment located novel genetic biomarkers, utilizing Hygieia, an open-source AI/ML pipeline to more comprehensively understand AMD’s etiology for potential treatments and assess the performance of this model in diagnosing AMD. A gene expression dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed to identify significant differentially expressed genes (DEGs) between case and control retinal samples. AI/ML analysis was performed using Hygieia to identify statistically significant genes. Gene ontology analysis was performed and a classifier was constructed to analyze the prediction performance of the genes in diagnosing AMD. Results were compared with current literature. The data from this experiment supported earlier findings linking RPE dysfunction and concurrent inflammatory mechanisms in AMD’s pathogenesis. Low p-values were obtained from the Chi-Square test for SLC1A4 (p,0.001), BCS1L (p,0.001), and SNHG17 (p
Utilizing Artificial Intelligence and Single-Cell RNA-seq Data for the Investigation and Discovery of Novel Genetic Biomarkers in Age-Related Macular Degeneration
Age-related macular degeneration (AMD) is a progressive neurodegenerative eye disorder characterized by eventual degeneration of the retinal pigment epithelium (RPE) leading to permanent vision loss. Artificial intelligence (AI) and machine learning (ML) have revolutionized healthcare by advancing clinical diagnosis leveraging its ability to analyze vast amounts of patient data and accurately predict future outcomes. With no definitive treatment for AMD, this experiment located novel genetic biomarkers, utilizing Hygieia, an open-source AI/ML pipeline to more comprehensively understand AMD’s etiology for potential treatments and assess the performance of this model in diagnosing AMD. A gene expression dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed to identify significant differentially expressed genes (DEGs) between case and control retinal samples. AI/ML analysis was performed using Hygieia to identify statistically significant genes. Gene ontology analysis was performed and a classifier was constructed to analyze the prediction performance of the genes in diagnosing AMD. Results were compared with current literature. The data from this experiment supported earlier findings linking RPE dysfunction and concurrent inflammatory mechanisms in AMD’s pathogenesis. Low p-values were obtained from the Chi-Square test for SLC1A4 (p,0.001), BCS1L (p,0.001), and SNHG17 (p