In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool
for tackling complex computational challenges. This review focuses on the transformative …

Transcriptional enhancers act as docking stations for combinations of transcription factors
and thereby regulate spatiotemporal activation of their target genes. It has been a long …

Deep neural networks (DNNs) have greatly advanced the ability to predict genome function
from sequence. However, elucidating underlying biological mechanisms from genomic …

Regulatory DNA sequences within enhancers and promoters bind transcription factors to
encode cell type-specific patterns of gene expression. However, the regulatory effects and …

Foundation models have achieved remarkable success in several fields such as natural
language processing, computer vision and more recently biology. DNA foundation models in …

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

The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene
expression has introduced challenges in their evaluation and interpretation. Current …

Genomic sequence-to-activity models are increasingly utilized to understand gene
regulatory syntax and probe the functional consequences of regulatory variation. Current …

The precise identification of splice sites is essential for unraveling the structure and function
of genes, constituting a pivotal step in the gene annotation process. In this study, we …

Neural networks have emerged as immensely powerful tools in predicting functional
genomic regions, notably evidenced by recent successes in deciphering gene regulatory …