The complex language of eukaryotic gene expression remains incompletely understood.
Despite the importance suggested by many noncoding variants statistically associated with …

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 …

Background Ensemble predictors such as the random forest are known to have superior
accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized …

Algorithms that accurately predict gene structure from primary sequence alone were
transformative for annotating the human genome. Can we also predict the expression levels …

Genome-wide association studies (GWASs) examine the association between genotype and
phenotype while adjusting for a set of covariates. Although the covariates may have non …

Most existing tools for constructing genetic prediction models begin with the assumption that
all genetic variants contribute equally towards the phenotype. However, this represents a …

Background To integrate molecular features from multiple high-throughput platforms in
prediction, a regression model that penalizes features from all platforms equally is …

Cancer detection from gene expression data continues to pose a challenge due to the high
dimensionality and complexity of these data. After decades of research there is still …

Motivation Computational methods that predict differential gene expression from histone
modification signals are highly desirable for understanding how histone modifications …

In recent years, numerous applications have demonstrated the potential of deep learning for
an improved understanding of biological processes. However, most deep learning tools …