Bridging the gap between the lab and computational analysis
We use an artificial intelligence approach to analyze large amounts of biological data and make meaningful predictions.
Data collection from a multi-omics analysis of fermentation or from sensors in an industrial bioprocess can easily result in terabytes of data. Managing this massive amount of information is one of the defining challenges for the computational analysis of biological systems.
Using deep learning, we mimic the activity of layers of neurons in a living brain to solve otherwise intractable computational problems.
Potential applications of deep learning include:
- Analysis of genomes for rational genomic engineering, resulting in improved efficiency in industrial applications
- Rational design of novel biological processes for new bioproducts
Deep learning streamlines the process for extracting meaningful information from biological observations. We can rapidly turn this into actionable information that can help guide continuous improvements to your bioprocess, or to optimize ongoing industrial fermentations in real time.
- Argonne National Laboratory
- Lawrence Berkeley National Laboratory