Although remarkable advances have been reported in high-throughput sequencing, the
ability to aptly analyze a substantial amount of rapidly generated biological …

Protein–ligand interactions are increasingly profiled at high throughput using affinity
selection and massively parallel sequencing. However, these assays do not provide the …

Current data generation capabilities in the life sciences render scientists in an apparently
contradicting situation. While it is possible to simultaneously measure an ever-increasing …

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on
clustering, while lacking the ability to explicitly model interactions between different feature …

We present a physics-based machine learning approach to predict in vitro transcription
factor binding affinities from structural and mechanical DNA properties directly derived from …

In recent years, the convolutional neural network (CNN) technique has emerged as an
efficient new method for designing porous structure, but a CNN model generally contains a …

Quantifying the nucleotide preferences of DNA binding proteins is essential to
understanding how transcription factors (TFs) interact with their targets in the genome. High …

Abstract Motivation Transcription factor (TF) DNA-binding is a central mechanism in gene
regulation. Biologists would like to know where and when these factors bind DNA. Hence …

Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that
allow a number of humans, plant and animal pathogens to inject virulence factors directly …

We present a novel unsupervised deep learning approach called BindVAE, based on
Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open …