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Agarwal and Shendure demonstrate that deep convolutional neural networks can effectively predict mRNA abundance from genomic sequence, achieving significant accuracy improvements over traditional models. Their model, Xpresso, utilizes promoter sequences and mRNA stability features, explaining up to 71% of variation in mRNA levels in mice and 59% in humans. The study highlights the predictive power of CpG dinucleotide content at core promoters and suggests future applications for cell-type-specific gene expr

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neelamdabassen15

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EN