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The integration of big data, artificial intelligence (AI), and shared databases is transforming rare disease research. Collectively, rare diseases impact over 300 million people globally, yet significant gaps in data hinder early diagnosis and treatment. The combination of technology and research is enabling scientists to uncover new genetic variants, leading to improved treatment options for patients.
Genomic databases, such as those created post-Human Genome Project, are crucial for advancing research in rare diseases. Initiatives like the GenomeAsia 100K project and MedGenome's SAS ATLAS database are expanding the scope of research by mapping genetic diversity, particularly in underrepresented populations. The future of rare disease research relies on continued investment in data infrastructure and collaboration among stakeholders.
• AI and big data enhance rare disease diagnosis and treatment options.
• Machine learning algorithms streamline genomic analysis for faster mutation identification.
AI technologies are utilized to automate data analysis, improving efficiency in rare disease research.
ML algorithms predict drug interactions and optimize clinical trial designs for rare diseases.
Genomic databases aggregate genetic data, aiding researchers in identifying disease-causing mutations.
MedGenome's platforms, like VarMiner, leverage AI to enhance genomic analysis and mutation identification.
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