top of page

ALaSCA Example Findings

ALaSCA is a cloud-hosted software platform that applies rigorous causal AI algorithms to omics and phenotypic datasets, with a goal of identifying key drivers in complex diseases. 


ALaSCA's causal AI algorithm (called Causal Inference) takes a Bayesian Network-based approach and was initially developed by Professor Judea Pearl (UCLA).  Causal Inference is regularly used outside the Life Sciences industry, but it is also very well suited to work analyze complex mechanisms in networks in an explainable way for scientists to understand and have confidence in. 


ALaSCA is a first-of-a-kind platform to effectively apply Causal Inference on protein pathways.  On November 6 2023, ALaSCA's initial OVC findings were announced.  Below is a short video overview of these results. 


Click here to explore the results directly.

For a background of how ALaSCA works, please check out our previous T1D demonstration:

- Video introduction

- BioRxiv publication

For more information about ALaSCA and our recent OVC study, please contact us at

bottom of page