DANN: a deep learning approach for annotating the pathogenicity of genetic variants

By Daniel Quang, Yifei Chen, Xiaohui S. Xie. 2015

972 citations recorded by the source corpus.

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In Genetics

In Molecular Biology

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Though our emphasis here is on the training strategy, it should be noted that logistic regression outperformed DANN when distinguishing known pathogenic mutations from likely benign variants in real data.”
Deep Learning for Health Informatics (2017, positive): “Deep learning approaches have also outperformed SVM in predicting splicing code and understanding how gene expression changes by genetic variants [48], [130].”
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach (2016, methodology): “For example, the deepnet-rbp fuses the structural and k-mer sequence features to predict RBP interaction sites [24] using DBNs. DANN trains a DBN to annotate non-coding variants [25], which is able to capture non-linear abstraction features.”
VarSome: the human genomic variant search engine (2018, background): “(8), Kaviar3 (4) and ICGC Somatic (5); pathogenicity predictions from dbNSFP (9), which compiles prediction scores from 20 different algorithms, and DANN (10).”
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “Other studies have shown that cell type-specific functional elements can display unique patterns of motif densities and epigenetic signals (Quang et al., 2015b).”
Gene expression inference with deep learning (2015, background): “With these advances, deep learning has achieved state-of-the-art performances on a wide range of applications, both in traditional machine learning tasks such as computer vision [13], natural language processing [14], speech recognition [15], and in natural science applications such as exotic partic”
GAVIN: Gene-Aware Variant INterpretation for medical sequencing (2017, methodology): “From the output VCF, we used the INFO fields “ PSNPE, ” “ FATE, ” “ GWAVAE, ” “ DANNE, ” and “ FUNE ” for the pathogenicity estimation outcomes according to the PredictSNP protocol for PredictSNP2 consensus, FATHMM, GWAVA, DANN, and FunSeq, respectively.”
Artificial intelligence in clinical and genomic diagnostics (2019, methodology): “…meta-predictors (models that process and merge the predictions produced by several other predictors) that outperform both their individual predictive components and the combination of those predictive components when integrated using regression or other machine-learning approaches [57].”
LIST-S2: taxonomy based sorting of deleterious missense mutations across species (2020, neutral): “0a (17) (namely: SIFT (1), SIFT4G (18), PROVEAN (2), phyloP (3) X 3, SiPhy (5), phastCons (6) X 3, GERP++ (4), Eigen (10), CADD (9), DANN (11),”