A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.
Europe PMC REST API · Indexed academic record · Source published: Thu Jun 25 2020 00:00:00 GMT+0000 (Coordinated Universal Time) · Indexed here: Thu Oct 08 2026 09:08:43 GMT+0000 (Coordinated Universal Time)
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Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences can however get stuck in local minima and often have low diversity. Here, we develop deep exploration networks (DENs), a class of activation-maximizing
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