Pioneering New Tools for Orphan Drug Makers
Genomenon Wins Bio-IT World 2023 Best of Show Award
New Rare Disease Prevalence Estimation Approach Awarded Bio-IT 2023 Best of Show
Each year, the Best of Show Awards program at Bio-IT World recognizes innovative product solutions to important problems facing the Life Sciences industry. Genomenon’s new Genomic Intelligence approach to improve and validate rare disease prevalence calculations was one of 33 new products considered for this prestigious award at At Bio-IT World 2023. It was a clear favorite of the judges.

Accurately estimating the prevalence of a rare disease is particularly challenging for orphan drug developers. Missing even one published paper that documents pathogenic and likely pathogenic gene variants implicated in rare genetic diseases can dramatically affect the resulting estimate. Underestimating disease prevalence can compromise fund-raising efforts for rare disease drug programs, whereas overestimating can make it difficult to meet clinical trial recruitment goals.
For companies targeting rare diseases, Genomenon’s disease prevalence report provides a more complete understanding of the genetic prevalence of autosomal recessive (AR) diseases. Part of Genomenon’s new Genomic Intelligence portfolio, this offering leverages both the Mastermind Genomic Language Processing (GLP) AI technology and exhaustive knowledgebase of human genomic evidence with genomic intelligence.
Read the full press release to learn why the Bio-IT awards committee was so impressed with our new approach to calculating rare disease prevalence.
Do You Need Genomic Intelligence?
The challenges of identifying relevant genetic information for precision medicine can be daunting, even for experienced genomic scientists and curators. For genetic diseases and cancer especially, every paper, every pathogenic variant, and every patient yields precious information.
If you are developing therapeutics for genetic diseases and cancer, talk to us. We are genomic intelligence experts, skilled at mining, analyzing, and reporting on genetic evidence needed to:
- Validate rare disease prevalence to assess market potential
- Identify genetic disease biomarkers and mechanisms
- Fast-track retrospective natural history studies
- Optimize clinical trial inclusion criteria
- Document evidence required for regulatory submissions