Using Artificial Intelligence To Discover Novel Biological Targets For Endocarditis Infections
Session Number
MEDH 09
Advisor(s)
Dr. John Thurmond, Illinois Mathematics and Science Academy
Discipline
Medical and Health Sciences
Start Date
17-4-2025 10:15 AM
End Date
17-4-2025 10:30 AM
Abstract
Enterococcus faecium is a gram-positive bacterium known to cause a variety of infections in humans, including endocarditis, a bacterial infection of the heart’s inner lining. PandaOmics is an AI driven platform for finding therapeutic biological targets of different diseases by aggregating biological and biomedical datasets to produce an organized list of potential biological targets for a specific disease based on a variety of criteria. The platform combines data of gene expression, methylation, and proteomics to create a ranked list of genes that are potential therapeutic targets, looking at factors such as small molecules, safety considerations, protein class, biological process involvement, novelty, and pharmaceutical development to identify the best candidates. PandaOmics was used to identify potential small molecule novelty biological targets of the disease endocarditis. Biological targets were mainly sorted based on being small molecule and novelty targets. The dataset created by PandaOmics identified specific genes as the best potential targets for endocarditis, as they matched a majority of criteria the AI search was looking for. These identified biological targets can then be further explored in a laboratory setting to better understand how endocarditis occurs in the body and create potential treatments for endocarditis infections.
Using Artificial Intelligence To Discover Novel Biological Targets For Endocarditis Infections
Enterococcus faecium is a gram-positive bacterium known to cause a variety of infections in humans, including endocarditis, a bacterial infection of the heart’s inner lining. PandaOmics is an AI driven platform for finding therapeutic biological targets of different diseases by aggregating biological and biomedical datasets to produce an organized list of potential biological targets for a specific disease based on a variety of criteria. The platform combines data of gene expression, methylation, and proteomics to create a ranked list of genes that are potential therapeutic targets, looking at factors such as small molecules, safety considerations, protein class, biological process involvement, novelty, and pharmaceutical development to identify the best candidates. PandaOmics was used to identify potential small molecule novelty biological targets of the disease endocarditis. Biological targets were mainly sorted based on being small molecule and novelty targets. The dataset created by PandaOmics identified specific genes as the best potential targets for endocarditis, as they matched a majority of criteria the AI search was looking for. These identified biological targets can then be further explored in a laboratory setting to better understand how endocarditis occurs in the body and create potential treatments for endocarditis infections.