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Selected Software
- EpiAnno (Nature Machine Intelligence, 2022, 4:116-126)
Annotates cell types by using a supervised mixture model through a Bayesian neural network.
- scDEC (Nature Machine Intelligence, 2021, 3:536-544)
Annotates cell types by using a set of two unsupervised generative adversarial neural networks (GAN).
- RA3 (Nature Communications, 2021, 12:2177)
Annotates cell types by using a weakly supervised generative model.
- DC3 (Nature Communications, 2019, 10:4613)
Annotates cell types by using a non-negative matrix factorization model.
- reCAT (Nature Communications, 2017, 8:22)
Constructs pseudo cell cycle by using a combinatorial model (the TSP problem).
- Roundtrip (Proc Natl Acad Sci USA, 2021, 15:e2101344118)
Estimates high-dimensional density.
- OpenCausal (Proc Natl Acad Sci USA, 2020, 117(35):21364-21372)
Prioritizes causal non-coding genetic variants via a regression model.
- PECA (Proc Natl Acad Sci USA, 2017, 114(25):E4914-E4923)
Constructs regulatory network via a regression model.
- DeepTACT (Nucleic Acids Research, 2019, 47(10):e60)
Predicts enhancer-promoter interactions via a bootstrapping neural network.
- OpenAnnotate (Nucleic Acids Research, 2021, 49(W1):W383-W490)
Provides ultra highly efficient annotation of chromatin accessibility.
- SilencerDB (Nucleic Acids Research, 2021, 49(D1):D221-D228)
The first comprehensive database of silencers.
Cell type identification
- EpiAnno (Nature Machine Intelligence, 2022, 4:116-126)
Annotates cell types by using a supervised mixture model through a Bayesian neural network.
- scDEC (Nature Machine Intelligence, 2021, 3:536-544)
Annotates cell types by using a set of two unsupervised generative adversarial neural networks (GAN).
- RA3 (Nature Communications, 2021, 12:2177)
Annotates cell types by using a weakly supervised generative model.
- DC3 (Nature Communications, 2019, 10:4613)
Annotates cell types by using a non-negative matrix factorization model.
- reCAT (Nature Communications, 2017, 8:22)
Constructs pseudo cell cycle by using a combinatorial model (the TSP problem).
- stPlus (Bioinformatics, 2021, 37:i299-i307)
Enhances spatial resolved transcriptome data.
Gene regulation analysis
- OpenAnnotate (Nucleic Acids Research, 2021, 49(W1):W383-W490)
Provides ultra highly efficient annotation of chromatin accessibility.
- SilencerDB (Nucleic Acids Research, 2021, 49(D1):D221-D228)
Is the first comprehensive database of silencers.
- EnDisease (Database, 2019, baz020)
Is a database and web server of disease-related enhancers.
- EnDisease (Database, 2019, baz020)
Provides manually curated disease-related enhancers.
- DeepTACT (Nucleic Acids Research, 2019, 47(10):e60)
Predicts enhancer-promoter interactions via a bootstrapping neural network.
- PECA (Proc Natl Acad Sci USA, 2017, 114(25):E4914-E4923)
Constructs regulatory network via a regression model.
- DC3 (Nature Communications, 2019, 10:4613)
Constructs gene regulatory networks at cell type level.
Disease gene prioritization
- OpenCausal (Proc Natl Acad Sci USA, 2020, 117(35):21364-21372)
Prioritizes causal non-coding genetic variants via a regression model.
- SPRING (PLoS Genetics, 2014, 10(3):e1004237)
Prioritizes nonsynonymous single nucleotide variants in exome sequencing studies.
- pgWalk (Journal of Molecular Cell Biology, 2015, 7(3):214-230)
Walks on multiple disease-gene networks to prioritize candidate genes.
- SIGNET (Journal of Molecular Cell Biology, 2017, 9(6):436-452)
Predicts disease-associated genes and tissues simultaneously.
- AlignPI (Bioinformatics, 2009, 25(1):98-104)
Predicts disease-associated genes via network alignment of disease network and gene network.
- MAXIF (Bioinformatics, 2011, 27:i167-i176)
Prioritizes candidate disease genes via network flow allocating in disease network and gene network.
- DRAGEN (Bioinformatics, 2015, 31(4):563-571)
Predicts disease-associated gene sets via a Markov random field.
- EnDisease (Database, 2019, baz020)
Provides manually curated disease-related enhancers.
- dbWGFP (Database, 2016, baw024)
Annotates human whole-genome single nucleotide variants and their functional predictions.
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