“Prolego applies novel artificial intelligence and machine learning methods to provide genomic predictions for thoroughbred animals and crops. Genomes refer to the hereditary information encoded in DNA this information. This information along with the pedigree information of the animals/crops are what is used to provide predictions of breeding values and performance metrics.
The company has built two proprietary algorithms that beat the accuracy of traditional genomic prediction methods by approximately 20%.
Prolego Scientific’s algorithms and software solutions can be used to improve the performance of genetic tests and improve breeding values of pedigree or thoroughbred animals. The technologies’ focus revolves around improving animal health and performance metrics, to improve the quality of the food chain and predict breeding values.
The team consists of world class leaders in areas such as statistics, algorithmic development, bio engineering and genetics as well as strategic and business development. Belinda Hernandez and Andrew Parnell are the two co-founders and primary investigators. Belinda plans to move into the CEO role after spin-out and Andrew Parnell who is a professor in NUIM will remain as CSO.
The company’s algorithms can be used in any industry which develops genomic breeding programmes or performs genetic testing of performance traits in thoroughbred animals or crops.
Currently, the Gen A technology is being used in the equine industry to predict the future performance metrics of elite thoroughbred racehorses such as their optimal racing conditions, race speed, and physical traits such as height and susceptibility to diseases. It has been shown to improve predictive accuracy by 20% on average over industry gold standard algorithms.
Prolego have a Gen B algorithm in development which aims to launch in 2019. Gen B is currently showing accuracy improvements of between 10-100% over the industry gold standard algorithms”.