J. T. Balewski

Deeplasmid uses deep learning to accurately distinguish plasmids from bacterial chromosomes in assembled genomes, achieving high precision and validating novel plasmid detection.

This paper uses CNNs to infer neuronal ionic conductances from simulated membrane potentials, achieving better accuracy than traditional optimization methods.

The paper trains a deep recurrent neural network to predict bacterial two-component signaling pairs, accurately recommending HK-RR pairs including orphans in Escherichia coli.