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Doctor AI2 – Artificial Intelligence mining of the Adaptive Immune system to develop an immunodiagnostics platform


Description:

Early diagnosis of disease is key to optimal treatment and in terms of diagnosis, our adaptive immune system is the “best doctor”. It carries out diagnosis with unmatched precision before clinical symptoms arise. There is a gold rush in academia and industry to develop artificial intelligence (AI) methods that exploit our immune system’s capacity to assist doctors in everyday diagnosis. The adaptive immune system records each past and ongoing battle with disease. This immune memory is recorded by “immune receptors” - short genetic sequences specific for each disease. Immune receptors can today be sequenced at high-throughput. We have previously shown that similar immune receptors (similar: identity of entire genetic sequence or subsequence) arise in different individuals when faced with the same disease. Thus, the pattern recognition capacity of machine learning may be leveraged to detect disease-associated patterns in the genetic sequences of immune receptors. However, so far, machine-learning-based exploitation for immunodiagnostics of immune receptor sequence datasets has been rather poor. This is due to (1) a lack of machine learning approaches that can exploit the unique biological characteristics of immune receptor repertoires, (2) and a lack of ground truth data for machine learning benchmarking.(3) There exists currently no platform for the machine learning analysis of large-scale immune receptor datasets. To resolve these knowledge gaps, we propose to develop novel AI methodology and implement a comprehensive software platform for immune receptor-based diagnostics. To validate our framework, we have access to the world-wide largest experimental and synthetic immune receptor datasets. In the medium-term horizon, the transdisciplinary project Doctor AI^2 will move the research frontier in AI techniques for immune-receptor immunodiagnostics and contributes to the AI revolution in medicine by supporting clinicians in therapeutic decision making.


Project leader: Geir Kjetil Sandve

Started: 2021

Ends: 2023

Category: Universiteter

Sector: UoH-sektor

Budget: 8752000

Institution: Institutt for informatikk

Address: Oslo