Predicting changes in gene activity when genes are altered involves understanding both individual gene roles and their interactions. The impact can range from isolated changes in a single gene's messenger RNA to widespread alterations due to gene regulation and metabolic shifts. With two altered genes, results can be additive or exhibit unexpected interactions. Researchers utilize CRISPR technology and RNA sequencing to examine these changes and apply machine learning models for predictions, training with specific datasets to enhance accuracy in forecasting results of gene interactions.
The task involves predicting gene activity changes when genes are altered, requiring understanding how individual genes and their interactions affect overall gene expression.
Altering gene activity with CRISPR allows for detailed RNA sequencing, providing data to train models that can predict subsequent gene activity changes.
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