RNA interference (RNAi) therapeutics have untapped potential for treating rare genetic diseases by silencing genes that drive these conditions. This approach allows scientists to target and deactivate specific genes, offering a customized strategy for dealing with genetic disorders. However, realizing the full potential of RNAi therapeutics hinges on overcoming a critical challenge: bridging data gaps to accelerate the identification of therapeutic targets.
Despite advancements in RNAi technology, the process of identifying relevant genes remains tedious and challenging. Researchers often face significant obstacles in accessing high-quality single-cell data and ensuring that these datasets are ready for AI-driven analysis. Challenges such as low-quality public datasets, inconsistent annotations, and the lack of structured storage systems for gene-silencing conditions make the path to discovery time-consuming and resource-intensive.
Source: https://www.elucidata.io/blog/streamlining-gene-identification-for-rnai-therapeutics-with-ai-ready-data
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