Applications of deep learning in understanding gene regulation
Gene regulation is a central topic in cell biology. Advances in omics technologies and the
accumulation of omics data have provided better opportunities for gene regulation studies …
accumulation of omics data have provided better opportunities for gene regulation studies …
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation
Sequence-based machine-learning models trained on genomics data improve genetic
variant interpretation by providing functional predictions describing their impact on the cis …
variant interpretation by providing functional predictions describing their impact on the cis …
The role of alternative polyadenylation in the regulation of subcellular RNA localization
Alternative polyadenylation (APA) is a widespread and conserved regulatory mechanism
that generates diverse 3′ ends on mRNA. APA patterns are often tissue specific and play …
that generates diverse 3′ ends on mRNA. APA patterns are often tissue specific and play …
Context-aware poly (a) signal prediction model via deep spatial–temporal neural networks
Polyadenylation [Poly (A)] is an essential process during messenger RNA (mRNA)
maturation in biological eukaryote systems. Identifying Poly (A) signals (PASs) from the …
maturation in biological eukaryote systems. Identifying Poly (A) signals (PASs) from the …
Multiplexed single-cell characterization of alternative polyadenylation regulators
Most mammalian genes have multiple polyA sites, representing a substantial source of
transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To …
transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To …
Deciphering the impact of genetic variation on human polyadenylation using APARENT2
Background 3′-end processing by cleavage and polyadenylation is an important and finely
tuned regulatory process during mRNA maturation. Numerous genetic variants are known to …
tuned regulatory process during mRNA maturation. Numerous genetic variants are known to …
Fast activation maximization for molecular sequence design
Background Optimization of DNA and protein sequences based on Machine Learning
models is becoming a powerful tool for molecular design. Activation maximization offers a …
models is becoming a powerful tool for molecular design. Activation maximization offers a …
A survey on methods for predicting polyadenylation sites from DNA sequences, bulk RNA-seq, and single-cell RNA-seq
Alternative polyadenylation (APA) plays important roles in modulating mRNA stability,
translation, and subcellular localization, and contributes extensively to sha** eukaryotic …
translation, and subcellular localization, and contributes extensively to sha** eukaryotic …
DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions
Motivation Accurate annotation of different genomic signals and regions (GSRs) from DNA
sequences is fundamentally important for understanding gene structure, regulation and …
sequences is fundamentally important for understanding gene structure, regulation and …
Interpreting neural networks for biological sequences by learning stochastic masks
Sequence-based neural networks can learn to make accurate predictions from large
biological datasets, but model interpretation remains challenging. Many existing feature …
biological datasets, but model interpretation remains challenging. Many existing feature …