AI-Driven Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics
Session Number
1
Advisor(s)
Dr. John Thurmond, Illinois Math & Science Academy
Location
A155
Discipline
Medical and Health Sciences
Start Date
15-4-2026 10:15 AM
End Date
15-4-2026 11:00 AM
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that causes the degeneration of motor neurons and leads to muscle weakness, paralysis, and eventually respiratory failure. Despite advances in understanding ALS genetics, effective treatments remain limited, highlighting the need to identify new therapeutic targets. This project investigates the use of PandaOmics, an artificial intelligence–driven target discovery platform, to identify genes and pathways associated with ALS progression. The platform integrates multi-omics datasets, including transcriptomic and proteomic data from post-mortem central nervous system tissues and induced pluripotent stem cells–derived motor neurons. By applying computational scoring systems and meta-analysis across multiple datasets, PandaOmics prioritizes candidate genes based on biological relevance, druggability, and novelty. Pathway analysis is then used to examine how these targets contribute to ALS-related cellular processes. This research evaluates how AI-based computational tools can accelerate the identification of potential therapeutic targets and improve understanding of the molecular mechanisms underlying ALS. The findings highlight the potential of integrating artificial intelligence with large-scale biological data to support future drug discovery efforts for neurodegenerative diseases.
AI-Driven Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics
A155
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that causes the degeneration of motor neurons and leads to muscle weakness, paralysis, and eventually respiratory failure. Despite advances in understanding ALS genetics, effective treatments remain limited, highlighting the need to identify new therapeutic targets. This project investigates the use of PandaOmics, an artificial intelligence–driven target discovery platform, to identify genes and pathways associated with ALS progression. The platform integrates multi-omics datasets, including transcriptomic and proteomic data from post-mortem central nervous system tissues and induced pluripotent stem cells–derived motor neurons. By applying computational scoring systems and meta-analysis across multiple datasets, PandaOmics prioritizes candidate genes based on biological relevance, druggability, and novelty. Pathway analysis is then used to examine how these targets contribute to ALS-related cellular processes. This research evaluates how AI-based computational tools can accelerate the identification of potential therapeutic targets and improve understanding of the molecular mechanisms underlying ALS. The findings highlight the potential of integrating artificial intelligence with large-scale biological data to support future drug discovery efforts for neurodegenerative diseases.