New system discovered to match genetic anomalies with precision medicine treatments.

‘eGARD analyses words and phrases in medical literature to find relationships between genomic anomalies and drug responses.’

However, until now, the data linking genetic factors and treatment results has been spread among hundreds of academic journals. It would take days for doctors, doing nothing else, to find and read all these reports. Now, they may be able to spend that time delivering optimized treatments instead. 




The promise of eGARD
eGARD is a text mining system that analyzes words and phrases in medical literature to find relationships between genomic anomalies and drug responses.
"Clinicians have no time to read all of the reports and literature for each tumor," said Peter McGarvey, a study author and associate professor of biochemistry at Georgetown University. "eGARD is a way to help surface the important ones for clinicians, medical geneticists or maybe companies that already are doing this in other ways."
The research team applied eGARD on roughly 36,000 article abstracts, retrieving 50 genes and 42 cancer drugs, including cell cycle inhibitors, kinase inhibitors and antibody treatments.
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Then, they trained it to look for text suggesting treatment outcomes, such as "significantly poorer response" or "survival rate." Next, they sought words and phrases connecting a genetic anomaly and outcome, such as "correlate," "associate" or "sensitize."
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"We hope this could make a difference for oncologists and cancer patients alike," said study author Vijay Shanker, a professor of computer and information sciences at UD.
UD researchers developed the code and data processing for eGARD, and clinically focused researchers at Georgetown provided use cases, terminology, curated datasets and insight on what information was most important to clinicians working in precision medicine. Both groups tested and refined the system.
The team will make a public interface for eGARD. It may also be incorporated into other software eventually.
Source-Eurekalert