Deep structured learning for variant prioritization in Mendelian diseases
Effective computer-aided or automated variant evaluations for monogenic diseases will
expedite clinical diagnostic and research efforts of known and novel disease-causing genes …
expedite clinical diagnostic and research efforts of known and novel disease-causing genes …
DeepPVP: phenotype-based prioritization of causative variants using deep learning
I Boudellioua, M Kulmanov, PN Schofield… - BMC …, 2019 - Springer
Background Prioritization of variants in personal genomic data is a major challenge.
Recently, computational methods that rely on comparing phenotype similarity have shown to …
Recently, computational methods that rely on comparing phenotype similarity have shown to …
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 …
A phenotype centric benchmark of variant prioritisation tools
D Anderson, T Lassmann - NPJ genomic medicine, 2018 - nature.com
Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian
diseases the challenge is to discover the single etiological variant among thousands of …
diseases the challenge is to discover the single etiological variant among thousands of …
DANN: a deep learning approach for annotating the pathogenicity of genetic variants
Annotating genetic variants, especially non-coding variants, for the purpose of identifying
pathogenic variants remains a challenge. Combined annotation-dependent depletion …
pathogenic variants remains a challenge. Combined annotation-dependent depletion …
Updated benchmarking of variant effect predictors using deep mutational scanning
BJ Livesey, JA Marsh - Molecular systems biology, 2023 - embopress.org
The assessment of variant effect predictor (VEP) performance is fraught with biases
introduced by benchmarking against clinical observations. In this study, building on our …
introduced by benchmarking against clinical observations. In this study, building on our …
DOMINO: using machine learning to predict genes associated with dominant disorders
In contrast to recessive conditions with biallelic inheritance, identification of dominant
(monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance …
(monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance …
[HTML][HTML] Improved pathogenicity prediction for rare human missense variants
The success of personalized genomic medicine depends on our ability to assess the
pathogenicity of rare human variants, including the important class of missense variation …
pathogenicity of rare human variants, including the important class of missense variation …
MVP predicts the pathogenicity of missense variants by deep learning
Accurate pathogenicity prediction of missense variants is critically important in genetic
studies and clinical diagnosis. Previously published prediction methods have facilitated the …
studies and clinical diagnosis. Previously published prediction methods have facilitated the …
An improved phenotype-driven tool for rare mendelian variant prioritization: benchmarking exomiser on real patient whole-exome data
Next-generation sequencing has revolutionized rare disease diagnostics, but many patients
remain without a molecular diagnosis, particularly because many candidate variants usually …
remain without a molecular diagnosis, particularly because many candidate variants usually …