Background Gene expression is a key intermediate level that genotypes lead to a particular
trait. Gene expression is affected by various factors including genotypes of genetic variants …

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

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 …

Genome-wide prediction (GWP) has become the state-of-the art method in artificial
selection. Data sets often comprise number of genomic markers and individuals in ranges …

Deep learning shows promise for predicting gene expression levels from DNA sequences.
However, recent studies show that current state-of-the-art models struggle to accurately …

Background Genome-wide marker data are used both in phenotypic genome-wide
association studies (GWAS) and genome-wide prediction (GWP). Typically, such studies …

Deep learning (DL) has emerged as a powerful tool to make accurate predictions from
complex data such as image, text, or video. However, its ability to predict phenotypic values …

Genome-wide association studies (GWAS) identify genetic variants associated with traits or
diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the …

Motivation: Large-scale gene expression profiling has been widely used to characterize
cellular states in response to various disease conditions, genetic perturbations, etc …

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 …