Abstract
Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.
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Cite this article
APA 7
Ghasemnejad, T., Argha, A., Grosser, M., Wang, J., Yang, M., Porntaveetus, T., Roscioli, T., Lovell, N. H., Aarabi, M., & Alinejad-Rokny, H. (2026). Large Language Model Agents for Evidence Based Genetic Disease Severity Classification. https://omanscience.com/en/articles/large-language-model-agents-for-evidence-based-genetic-disease-severity-classification
MLA 9
Ghasemnejad, Tohid, et al. "Large Language Model Agents for Evidence Based Genetic Disease Severity Classification." https://omanscience.com/en/articles/large-language-model-agents-for-evidence-based-genetic-disease-severity-classification.
Chicago (author–date)
Ghasemnejad, Tohid, Ahmadreza Argha, Mark Grosser, John Wang, Min Yang, Thantrira Porntaveetus, Tony Roscioli, Nigel H. Lovell, Mahmoud Aarabi, and Hamid Alinejad-Rokny. 2026. "Large Language Model Agents for Evidence Based Genetic Disease Severity Classification." https://omanscience.com/en/articles/large-language-model-agents-for-evidence-based-genetic-disease-severity-classification.
Harvard
Ghasemnejad, T., Argha, A., Grosser, M., Wang, J., Yang, M., Porntaveetus, T., Roscioli, T., Lovell, N. H., Aarabi, M. and Alinejad-Rokny, H. (2026) 'Large Language Model Agents for Evidence Based Genetic Disease Severity Classification', Available at: https://omanscience.com/en/articles/large-language-model-agents-for-evidence-based-genetic-disease-severity-classification.
Vancouver
Ghasemnejad T, Argha A, Grosser M, Wang J, Yang M, Porntaveetus T, et al. Large Language Model Agents for Evidence Based Genetic Disease Severity Classification. https://omanscience.com/en/articles/large-language-model-agents-for-evidence-based-genetic-disease-severity-classification
IEEE
T. Ghasemnejad, A. Argha, M. Grosser, J. Wang, M. Yang, T. Porntaveetus, T. Roscioli, N. H. Lovell, M. Aarabi, and H. Alinejad-Rokny, "Large Language Model Agents for Evidence Based Genetic Disease Severity Classification," https://omanscience.com/en/articles/large-language-model-agents-for-evidence-based-genetic-disease-severity-classification.