Data Science and Computational Biology Offer New Tools for Rare Disease Diagnosis and Treatment
On Rare Disease Day, we are reminded of the impact these conditions have on those affected by them. While the term "rare" comes from the fact that each condition has a low prevalence, the overall impact of rare diseases should not be underestimated. In the European Union, a disease affecting fewer than 1 in 2,000 people qualifies as rare, yet over 7,000 such diseases have been identified, affecting an estimated 300 million individuals globally.
The burden of these rare conditions on patients’ quality of life is aggravated by the long average time to diagnosis (4-8 years) and the lack of available treatments – only 5% of rare diseases have an approved therapy (The Lancet Global Health, 2024).
In this context, data science and computational biology/bioinformatics may provide solutions that have the potential to improve improve the lives of patients and their access to better care.
Data science, through its advanced analytics, machine learning, and artificial intelligence capabilities, can extract valuable insights from health data. This allows for:
- Identification of disease patterns: leading to more accurate and timely diagnoses.
- Discovery of potential drug candidates: accelerating the development of effective treatments.
- Analysis of treatment outcomes: enabling personalized treatment plans, maximizing benefit and minimizing risk for each patient.
Since 80% of rare diseases have a genetic basis, computational biology provides invaluable tools for understanding them at the molecular level. By analyzing genomic data, it facilitates the identification of:
- Genetic variations associated with rare diseases: which can help explain the disease process.
- Potential biomarkers: for diagnosis, assessing disease severity, monitoring progression, and predicting treatment response.
- Biological pathways involved: leading to a deeper understanding of disease mechanisms and the development of targeted therapies.
- New therapies: development of novel gene therapies can be accelerated with the help of in silico models
Challenges remain, such as limited patient numbers due to the rarity of these diseases, diagnostic difficulties that further hinder accurate data collection, and poor data management practices. However, data science and computational biology hold immense potential to transform healthcare for those living with rare diseases, offering hope for a brighter future.
