As a data-driven science, genomics largely utilizes machine learning to capture
dependencies in data and derive novel biological hypotheses. However, the ability to extract …

Abstract JASPAR (http://jaspar. genereg. net) is an open-access database of curated, non-
redundant transcription factor (TF)-binding profiles stored as position frequency matrices …

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the
cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that …

Deep learning is a powerful tool for predicting transcription factor binding sites from DNA
sequence. Despite their high predictive accuracy, there are no guarantees that a high …

Most mammalian genes have multiple polyA sites, representing a substantial source of
transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To …

Deep convolutional neural networks (CNNs) trained on regulatory genomic sequences tend
to build representations in a distributed manner, making it a challenge to extract learned …

Although convolutional neural networks (CNNs) have been applied to a variety of
computational genomics problems, there remains a large gap in our understanding of how …

Motivation Genome-wide profiles of chromatin accessibility and gene expression in diverse
cellular contexts are critical to decipher the dynamics of transcriptional regulation. Recently …

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary
to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to …

Abstract Summary Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have
been used to learn predictive models of regulatory DNA sequence. However, interpreting …