Rare Variant Pathogenicity Classification and Phenotypic Pattern Recognition: AI-Driven Computational Approaches to Early Detection of Genetic Disorders

Authors

  • Giovanna Di Guglielmo Associate Professor of Information Engineering, University of Pisa

Keywords:

rare variant pathogenicity classification, phenotypic pattern recognition, computational approaches to early detection, genetic disorders, machine learning

Abstract

Genetic disorders are becoming increasingly common, with 4–6% and 36% of children being born with a major or minor genetic disorder, respectively. These disorders affect an individual’s mental and physical health and lead to significant levels of stress in families. Early detection is essential as it can lead to early intervention that can prevent or ameliorate many of these disorders.

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Published

31-12-2025

How to Cite

[1]
“Rare Variant Pathogenicity Classification and Phenotypic Pattern Recognition: AI-Driven Computational Approaches to Early Detection of Genetic Disorders”, J. Computational Intel. & Robotics, vol. 5, no. 2, pp. 1–8, Dec. 2025, Accessed: Jun. 04, 2026. [Online]. Available: https://thesciencebrigade.com/jcir/article/view/726