Obtaining genetics insights from deep learning via explainable artificial intelligence
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 …
for making functional predictions in genomics research. However, the underlying basis on …
A review of deep learning applications in human genomics using next-generation sequencing data
WS Alharbi, M Rashid - Human Genomics, 2022 - Springer
Genomics is advancing towards data-driven science. Through the advent of high-throughput
data generating technologies in human genomics, we are overwhelmed with the heap of …
data generating technologies in human genomics, we are overwhelmed with the heap of …
Scaffolding protein functional sites using deep learning
The binding and catalytic functions of proteins are generally mediated by a small number of
functional residues held in place by the overall protein structure. Here, we describe deep …
functional residues held in place by the overall protein structure. Here, we describe deep …
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 …
CADD v1. 7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions
Abstract Machine Learning-based scoring and classification of genetic variants aids the
assessment of clinical findings and is employed to prioritize variants in diverse genetic …
assessment of clinical findings and is employed to prioritize variants in diverse genetic …
Base-resolution models of transcription-factor binding reveal soft motif syntax
The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the
cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that …
cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that …
Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers
Background The largest sequence-based models of transcription control to date are
obtained by predicting genome-wide gene regulatory assays across the human genome …
obtained by predicting genome-wide gene regulatory assays across the human genome …
Controlling gene expression with deep generative design of regulatory DNA
Abstract Design of de novo synthetic regulatory DNA is a promising avenue to control gene
expression in biotechnology and medicine. Using mutagenesis typically requires screening …
expression in biotechnology and medicine. Using mutagenesis typically requires screening …
An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability
Genome-wide association studies have identified thousands of noncoding variants
associated with human traits and diseases. However, the functional interpretation of these …
associated with human traits and diseases. However, the functional interpretation of these …
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 …