The knowledge-driven MARRVEL AI-based model (AIM) to identify Mendelian illnesses.
By Pooja Toshniwal PahariaApr 30 2024Reviewed by Lily Ramsey, LLM In a recent study published in NEJM AI, researchers developed the artificial intelligence -based Model Organism Aggregated Resources for Rare Variant ExpLoration model to select causal genes and their mutations for Mendelian illnesses based on clinical characteristics and genetic sequences.
Bioinformatics-based re-assessment is less expensive but has limited accuracy, making it tedious to prioritize non-coding variations, and requires using simulation data. Researchers collected Human Phenotype Ontology keywords and exome sequences from three patient groups: DiagLab, the Undiagnosed Disease Network , and the Deciphering Developmental Disorders Project. They divided DiagLab data into training and testing datasets and tested DDD and UDN separately.
They used the "feature climbing" approach to assess the contribution of each feature and classify all characteristics according to their biological significance. Results AIM dramatically increased genetic diagnostic accuracy, tripling the number of solved cases relative to benchmarked approaches in three real-world cohorts. AIM attained a 98% accuracy rate and detected 57% of diagnoseable out of 871.
Artificial Intelligence Bioinformatics Diagnostic Gene Genes Phenotype Splicing
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