The enabling technology behind all Galenvs products is our patented machine learning and simulation platform, the industry’s only platform that predicts optimized reagents by yielding casual insights of ideal formulation parameters.
We believe the biggest opportunity to address inefficiencies in research and clinical labs is by utilizing computational modeling systems to derive new insights to optimize biomedical products and technologies, and in turn, create turn-key platforms and tools.
To create superior products, Galenvs employs Machine Learning training data sets for several critical variables affecting the performance of magnetic nanoparticles, buffers, and reagents.
We combine both in-house-generated and private data (derived from laboratory researchers and clinicians) sources to build data models that produce data correlations of predictive analytics and deep learning to reveal optimized magnetic nanoparticle and reagent formulations.
Galenvs’ proprietary AI driven system, guided by knowledge acquired from our seasoned scientists and bioinformaticians, actively learns best practice from these vast repositories of research data and predicts optimal formulations for innovative applications in a fast and accurate manner.
With better information to hand than any researcher could acquire individually, our knowledge-driven system designs hundreds of novel, project-specific formulations and pre-assess each for predicted yield, selectivity, and other key criteria. From this, a selection of the best, information-rich formulations are selected for synthesis and assay.
Of the hundreds of thousands of data sets we have generated and acquired, we have created hundreds of computational models for nucleic acid extraction and magnetic nanoparticle development. What this means is that we’ve initiated the discovery process in hundreds of research areas, such as blood, bacteria, soil, and clinical samples. Out of this pool of opportunities, we have dozens of kits under active development and progressing to market.
Our non-traditional AI-driven development approach has been shown to be the most powerful tool for research effectiveness simulation, speeding the process up by a number of months, and enabling a discovery methodology that increases technology efficiency dramatically.
Our system has already delivered exceptional productivity, generating successful formulations in roughly one-quarter of the time of traditional approaches.