Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk
Key challenges for human genetics, precision medicine and evolutionary biology include
deciphering the regulatory code of gene expression and understanding the transcriptional …
deciphering the regulatory code of gene expression and understanding the transcriptional …
Effective gene expression prediction from sequence by integrating long-range interactions
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 …
problem, and critical downstream applications in human genetics depend on improved …
Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification
Identifying functional variants underlying disease risk and adoption of personalized
medicine are currently limited by the challenge of interpreting the functional consequences …
medicine are currently limited by the challenge of interpreting the functional consequences …
Genome-wide prediction of disease variant effects with a deep protein language model
Predicting the effects of coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …
models have improved variant effect prediction accuracy, they cannot analyze all coding …
Predicting effects of noncoding variants with deep learning–based sequence model
J Zhou, OG Troyanskaya - Nature methods, 2015 - nature.com
Identifying functional effects of noncoding variants is a major challenge in human genetics.
To predict the noncoding-variant effects de novo from sequence, we developed a deep …
To predict the noncoding-variant effects de novo from sequence, we developed a deep …
Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data
Many genetic variants that influence phenotypes of interest are located outside of protein-
coding genes, yet existing methods for identifying such variants have poor predictive power …
coding genes, yet existing methods for identifying such variants have poor predictive power …
Disease variant prediction with deep generative models of evolutionary data
Quantifying the pathogenicity of protein variants in human disease-related genes would
have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of …
have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of …
regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants
S Zhang, Y He, H Liu, H Zhai, D Huang… - Nucleic acids …, 2019 - academic.oup.com
Predicting the functional or pathogenic regulatory variants in the human non-coding genome
facilitates the interpretation of disease causation. While numerous prediction methods are …
facilitates the interpretation of disease causation. While numerous prediction methods are …
A method to predict the impact of regulatory variants from DNA sequence
Most variants implicated in common human disease by genome-wide association studies
(GWAS) lie in noncoding sequence intervals. Despite the suggestion that regulatory element …
(GWAS) lie in noncoding sequence intervals. Despite the suggestion that regulatory element …
Decoding disease: from genomes to networks to phenotypes
AK Wong, RSG Sealfon, CL Theesfeld… - Nature Reviews …, 2021 - nature.com
Interpreting the effects of genetic variants is key to understanding individual susceptibility to
disease and designing personalized therapeutic approaches. Modern experimental …
disease and designing personalized therapeutic approaches. Modern experimental …