Data benchmark
Last updated
Last updated
After defining your assays, uploading data, and setting objectives, you can run a benchmark to evaluate how well your data fits Cradle's models. This process provides insights into your data quantity, quality and how well Cradle's models fit.
The data benchmark results in a report with insightful metrics and graphs that indicate:
Whether the data quantity and quality is sufficient for machine learning.
The performance of Cradle's machine learning algorithms in learning from your data.
This process helps both you and Cradle determine how well Cradle's models learn from your data and whether the project is worth pursuing.
After your data is uploaded, click on Start benchmark. The platform will show you how much time it will take to run the benchmark.
Once the benchmark report is ready you will receive a notification from Cradle. Log in to the platform to view your report.
Note: the benchmark report is different for each customer and tailored to your experimental data
The benchmark helps indicate the following possible results and next steps:
Good fit: The quantity and quality of the data are good enough to enable strong performance of machine learning models. We suggest moving forward with generating & testing sequences for this project.
Discussion needed: The quantity or quality of the data does not enable us to train a machine learning model that has good performance. We either need to find more data, use the data in a different way, or start the project with a zero-shot round instead (where sequences are generated from a large model of the generic protein landscape). Customer Success & a machine learning engineer will have an in-depth discussion with you to make a decision.
After you have completed the benchmark and want to use Cradle for your project, configure Rounds and proceed to Sequence generation.
