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Found 5 papers, 2 papers with code
Deciphering the regulatory genome of $\textit{Escherichia coli}$, one hundred promoters at a time
Then, we examine the regulatory architectures for more than 80 promoters in the ${\it E. coli}$ genome which previously had no known regulation.
Biophysical models of cis-regulation as interpretable neural networks
The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce.
Learning quantitative sequence-function relationships from massively parallel experiments
An analytically tractable model of a massively parallel experiment is then described, providing an explicit demonstration of these fundamental aspects of statistical inference.
Unification of field theory and maximum entropy methods for learning probability densities
Here I unify these two methods by showing that every maximum entropy density estimate can be recovered in the infinite smoothness limit of an appropriate Bayesian field theory.
Rapid and deterministic estimation of probability densities using scale-free field theories
The question of how best to estimate a continuous probability density from finite data is an intriguing open problem at the interface of statistics and physics.
