Researchers at Natural Selection will adapt neural network architectures for the reliable and accurate prediction of functional RNA (fRNA) genes including microRNAs. The effort, funded under a phase II Small Business Innovation Research (SBIR) grant, seeks to extend and improve previous methods of fRNA gene discovery.
RNA therapies have the potential to treat a wide range of human illnesses, as it has the potential to regulate or silence the activities of specific genes that cause disease when their activities become inappropriate.
“As the number of experimentally verified siRNAs, stRNAs, and microRNAs continues to increase, the rate at which we verify these elements is largely dependent on the prediction of interesting regions followed by laboratory analysis,” said Dr Fogel, vice president of Natural Selection, and principal investigator on the SBIR.
“Laboratory analysis is slow and costly, and thus a key to increasing the throughput rate for discovery is in the accurate algorithmic prediction of regions of interest. The product resulting from this phase II research will increase the predictive accuracy and the rate of fRNA discovery,” added Dr Fogel.