Our mission
The burden of auto-immune diseases
There are more than 80 different auto-immune diseases, affecting 3 to 5% of the population around the world. Type 1 diabetes, lupus, rheumatoid arthritis, inflammatory bowel disease and multiple sclerosis are the most common of these conditions.
Despite significant advances in understanding and classifying auto-immune diseases in the past decades, the challenge of heterogeneity remains a considerable. Heterogeneity impact both the onset and progression, diagnosis and prognosis of these diseases.
The data revolution
A multitude of anonymized data about patients with auto-immune conditions are generated everyday, including demographic information, disease activity index, biological data, images, treatments amongst others.
With breakthrough advancements in artificial intelligence and machine learning techniques, the possibility of translating this abundance of information into clinically relevant patterns is slowly becoming a reality.
The Scienta Lab expertise
At Scienta Lab, we are training our algorithms to identify patients, predict diagnosis and risk scores, and classify patients within auto-immune disease subgroups.
We are doing this by translating chaos into insights; yielding the largest ecosystem of datasets, scientific partnerships and machine learning capacities known to date.
The similarity of data across auto-immune diseases allows us to target develop a disease-targeted aproach, yet scale our models across the whole therapeutic area of immunology.
