Artificial intelligence (AI) models based on deep learning now represent the state of the art
for making functional predictions in genomics research. However, the underlying basis on …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Sequence-based machine-learning models trained on genomics data improve genetic
variant interpretation by providing functional predictions describing their impact on the cis …

How noncoding DNA determines gene expression in different cell types is a major unsolved
problem, and critical downstream applications in human genetics depend on improved …

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal
phenotype and fitness,–. Constructing complete fitness landscapes, in which DNA …

Genomic deep learning models can predict genome-wide epigenetic features and gene
expression levels directly from DNA sequence. While current models perform well at …

Motivation Deciphering the language of non-coding DNA is one of the fundamental
problems in genome research. Gene regulatory code is highly complex due to the existence …

High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …

Deep learning methods have recently become the state of the art in a variety of regulatory
genomic tasks,,,,–, including the prediction of gene expression from genomic DNA. As such …

We introduce a modern Hopfield network with continuous states and a corresponding
update rule. The new Hopfield network can store exponentially (with the dimension of the …