Technology

Deep Machine Learning + big data
(medical & "omics") = precision medicine

This is how we clarify clinical choices.

Scalability

allows for additional products development and necessary for future business growth

Mobile applications

capture patient views of how side-effects impact their life, informing medical decisions

Cybersecurity

Protection of all clinical and genomic information meets current regulatory standards. We conduct continuous quality review for updating new regulations along with external threats to prevent  unauthorized access (public website cannot access personal health or genomic information)

Algorithms

Comprehensive, state-of-the-art machine learning
  • Started with millions of SNPs per person along with clinical outcome of side effects.
  • Identified SNP networks have <100 SNPs/side effect
  • Each side effect per regimen has a unique, predictive SNP network with accuracy rates >90% in training set; validation set accuracy rates with small sample sizes yet still >70%
  • Mapping each SNP in the network for each side effect to its known genes & associated biologic pathways demonstrated biologic validity.

Architecture

Proprietary code driving custom analytics
  • Allows incorporation of new machine learning methods (including deep learning techniques) into our models rather than waiting for new releases or updates of “off the shelf” computing solutions
  • Facilitates more rapid product cycle development
  • Analytic cloud platform
  • Tailored to Inform Genomics specific business needs