Abstract:
Rare diseases, also referred to as orphan diseases, are medical conditions
characterized by their low prevalence within a specific population (generally 1 in 10.000).
The rarity of these diseases presents challenges in terms of accurate diagnosis, availability of
effective treatments, and adequate research funding. These conditions encompass a diverse range of
complex disorders caused by genetic mutations.
Artificial intelligence (AI) plays a vital role in rare disease annotation using its data analysis,
pattern recognition and knowledge integration capabilities
Objectives: Develop or use an AI-powered tool to accurately annotate and prioritize genetic
variants associated with a particular rare disease, aiding researchers, clinicians, and geneticists in
understanding the disease's genetic basis and potential treatment options.
Methods: Collect and integrate relevant genetic databases, medical literature, and clinical
trial data related to the specific rare disease.
Implement machine learning algorithms to identify and classify genetic variants from patient
data and reference genomes
Results and discussion: The use of these tools accelerates the identification and prioritization
of relevant genetic variants, reducing the time needed for manual analysis.
Conclusion: Having observed the positive outcomes resulting from the use of AI approach, it
prompt us to develop our work further and try to create an advanced AI solution that significantly
contributes to annotating genetic variants responsible for specific rare diseases
