A rare disease is defined in the US as a disease affecting less than 200,000 people. It is estimated that 400 million people globally are affected by rare diseases. Most rare diseases have a genetic etiology, but it takes an average of 5 years for rare disease patients to receive an accurate diagnosis. Moreover, nearly 95% of rare diseases lack an FDA-approved treatment. 30% of children with a rare disease are not expected to see their fifth birthday because of diagnostic delay and lack of effective therapeutic interventions.
On the other hand, ever-increasing amounts of data collected and managed for genetics and biomedical evidence present new opportunities for breakthroughs to improve diagnosis and treatments in rare diseases. Advanced biomedical informatics approaches hold great promise to support diagnosis, drug discovery, and clinical trials.
The ability of computational technologies to identify novel patterns in data, particularly data from different sources (e.g., multi-omics, patient registries, and so on), can be applied to overcome current challenges (e.g., poor diagnostic rates, lack of treatment standards, misunderstood etiology, and so on). Innovations in science, technology, and the use of big data and AI analytics are enabling great leaps forward with rare disease knowledge and treatment. We aim to seek unpublished investigations that introduce novel computational approaches and/or techniques developed to support scientific research and clinical decision-making in rare diseases.
1. Rare disease-based data management.
o Data curation
o Data normalization
o Data harmonization
o Data repository and sharing
2. Rare disease-relevant data retrieval via Natural Language Processing.
o Information retrieval
o Information extraction
3. Medical informatics in rare disease.
o Orphan drug discovery
o Drug repositioning
o Clinical decision support applications for rare diseases.
4. Bioinformatics in rare diseases.
o Analysis of high-throughput data in rare diseases research
o Integration of multi-omics data in rare diseases
5. Clinical decision support for rare diseases.
A rare disease is defined in the US as a disease affecting less than 200,000 people. It is estimated that 400 million people globally are affected by rare diseases. Most rare diseases have a genetic etiology, but it takes an average of 5 years for rare disease patients to receive an accurate diagnosis. Moreover, nearly 95% of rare diseases lack an FDA-approved treatment. 30% of children with a rare disease are not expected to see their fifth birthday because of diagnostic delay and lack of effective therapeutic interventions.
On the other hand, ever-increasing amounts of data collected and managed for genetics and biomedical evidence present new opportunities for breakthroughs to improve diagnosis and treatments in rare diseases. Advanced biomedical informatics approaches hold great promise to support diagnosis, drug discovery, and clinical trials.
The ability of computational technologies to identify novel patterns in data, particularly data from different sources (e.g., multi-omics, patient registries, and so on), can be applied to overcome current challenges (e.g., poor diagnostic rates, lack of treatment standards, misunderstood etiology, and so on). Innovations in science, technology, and the use of big data and AI analytics are enabling great leaps forward with rare disease knowledge and treatment. We aim to seek unpublished investigations that introduce novel computational approaches and/or techniques developed to support scientific research and clinical decision-making in rare diseases.
1. Rare disease-based data management.
o Data curation
o Data normalization
o Data harmonization
o Data repository and sharing
2. Rare disease-relevant data retrieval via Natural Language Processing.
o Information retrieval
o Information extraction
3. Medical informatics in rare disease.
o Orphan drug discovery
o Drug repositioning
o Clinical decision support applications for rare diseases.
4. Bioinformatics in rare diseases.
o Analysis of high-throughput data in rare diseases research
o Integration of multi-omics data in rare diseases
5. Clinical decision support for rare diseases.
