OncoNeo400 – bioaiworks.com

An AI engine with multiple AI systems such as PEP92, H92 and B92 able to predict the peptide/antigen immunogenicity towards MHC I and MHC II complexes. Our goal is to create one of the largest databases of AI generated peptide vs immune system predictions. NIAI engine has an accuracy of 92% at the current state

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One of the largest antigen prediction datasets / databases were used to train the models. IEDB datasets were used for training the AI system. (https://www.iedb.org/)

NetAgent uses the automation features to do mass predictions, potentially being optimized as a tool for finding wide spectrum cancer neoantigens by predicting experimentally validated neoantigen recognition probability

Datasets vs AI learning

The PEP92 was trained using various Real world experiments to be able to predict the binding affinity towards immune system cells

Mass prediction capabilities of peptide antigens in Oncology Research. The complexity of Oncology as disease requires aproaches of mass prediction AI systems. NetAgent was built to enable 100 000s of thousands of predictions with a click of a button.

AI Generated Datasets are curated by our domain experts to make sure that the Biological, Research, Biostatistical and Data Science perspectives match the real data.

Datasets as products

This segment of the platform is focused on offering specifically tailored AI generated
Datasets as products