Genomenon Inc., a DNA interpretation software company launched from the University of Michigan in FY 2015, recently announced the completion of its extended seed financing of $1.8 million. The financing will fuel the completion and launch of Genomenon’s initial product, Mastermind, a novel analytic and visualization tool for DNA variant interpretation. Investors included two University of Michigan funds, as well as the Michigan Angel Fund and an angel group formed by Rehmann owners.
Mastermind is used for clinical diagnostics and academic research to eliminate the DNA analysis bottleneck in cancer and genetic disease diagnostics. It reduces the time pathologists and geneticists spend researching and interpreting genetic variants by automatically mining millions of medical publications to find correlations between genes, variants required for clinical diagnosis.
“The fundamental bottleneck in diagnosing cancer and genetic diseases is the time spent manually researching DNA mutations” said Mark Kiel MD, Founder and CSO of Genomenon. “We have been able to dramatically reduce the time pathologists and geneticists spend interpreting genetic mutations by automatically mining millions of medical publications to find correlations between genes, variants required for clinical diagnosis.”
Genomenon spun out of the University of Michigan in 2015 which investments from two different university funds, MINTS (Michigan Investment in New Technology Start-ups), and the newly formed Monroe-Brown Fund.
The company originally set out to raise $1.0M in new financing and with the strong interest, the investment was oversubscribed.
“We’re really grateful to the Michigan start-up and investment community” said Mike Klein, CEO of Genomenon (pictured right). “It’s a testimony to the eco-system in Michigan that companies like Genomenon can spin great technology out of the university and find local funding to launch a successful venture.”
Mastermind is a comprehensive knowledge-base built on mining millions of medical publications that automatically finds correlations between genes, variants and diseases tied to the primary scientific literature required for clinical diagnosis of cancer and genetic diseases. It is due to launch in Q1 2017.