BioLib is the platform for distributing your bioinformatics application.
BioLib provides a one-stop-shop for distributing bioinformatics tools, making it easy to reach users and monetize models, while safeguarding your intellectual property.
With BioLib, it is easy for users to find, purchase, and use your applications.
BioLib unlocks a new way of collaboration, enabling you to commercialize proprietary models and tools.
With BioLib's privacy-preserving compute technology, no-one, but you can access your proprietary data.
All you need to build your application is some executable code and a BioLib account. Currently, BioLib supports Python, Rust, and C/C++. If you do not have any source code in one of the supported language, you can try creating an app with the example covered in this blog post. If you do not have an account yet, you can sign up here.
Your finished project will be an application that runs in the web-browser. End-users of your app will be able to run the app without installing anything, writing any code, or worrying about dependencies. You can keep the app completely private, share it with just your colleagues, or make the app publicly available for the world.
BioLib is the first platform that harnesses homomorphic encryption for the analysis of biological data. For developers of proprietary tools and models, this means that models, tools, and data can be monetized without exposing intellectual property.
Using BioLib's zero-knowledge compute platform, companies, organizations, and individuals, can offer analytics as a service, without users, or even the platform administrators, being able to access the analytics providers' proprietary code.
Fully Homomorphic Encryption (FHE) lets data holders and analytics providers collaborate without compromising sensitive data or proprietary models. Data is preprocessed and encrypted client-side, generating one public key and one private key. The public key allows the analysis provider to apply their model on the encrypted data without having access to the plaintext data or the analysis results. When the encrypted result is returned to the client, it is decrypted using the private key.
No-one but the client ever has access to the unencrypted data or result. No-one but the analysis provider ever has access to their proprietary models.