Dexiong Chen

A structure-conditioned nucleotide-level Potts model designs protein and coding sequences jointly, enabling tRNA adaptation and RNA motif embedding beyond synonymous codons with designability comparable to Caliby.

The paper develops a machine learning model to detect pOXA-48-carrying Enterobacterales using MALDI-TOF MS spectra, achieving high accuracy across different cohorts.

This study uses 1D-CNNs to predict PCR amplification bias from DNA sequences, identifying self-priming as a previously overlooked mechanism.

This paper introduces FDDC, a method for inferring connectivity in neuronal networks, showing it outperforms existing methods and reveals a negative correlation between small-worldness and network synchrony.

This paper introduces a hybrid model combining convolutional neural networks and kernel methods for biological sequence analysis, showing improved performance on small datasets and interpretability for motif discovery.