:: DESCRIPTION
DeepGS is a R package for predicting phenotypes from genotypes using deep learning techniques.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Ma W, Qiu Z, Song J, Li J, Cheng Q, Zhai J, Ma C.
A deep convolutional neural network approach for predicting phenotypes from genotypes.
Planta. 2018 Nov;248(5):1307-1318. doi: 10.1007/s00425-018-2976-9. Epub 2018 Aug 12. PMID: 30101399.
:: DESCRIPTION
DeepRescore is an immunopeptidomics data analysis tool that leverages deep learning-derived peptide features to rescore peptide-spectrum matches (PSMs).
::DEVELOPER
:: SCREENSHOTS
n/a
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Li K, Jain A, Malovannaya A, Wen B, Zhang B.
DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics.
Proteomics. 2020 Nov;20(21-22):e1900334. doi: 10.1002/pmic.201900334. Epub 2020 Sep 27. PMID: 32864883; PMCID: PMC7718998.
:: DESCRIPTION
AutoRT is a peptide sequence-based RT prediction tool developed using automated deep learning and transfer learning. It can provide high accurate RT prediction with models trained using a small number of peptides (~1000) with transfer learning.
::DEVELOPER
:: SCREENSHOTS
n/a
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Wen B, Li K, Zhang Y, Zhang B.
Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis.
Nat Commun. 2020 Apr 9;11(1):1759. doi: 10.1038/s41467-020-15456-w. PMID: 32273506; PMCID: PMC7145864.
:: DESCRIPTION
DeepFun is a deep-learning-based model for functional evaluation of genetic variants at the single-base resolution.
::DEVELOPER
Bioinformatics and Systems Medicine Laboratory
:: SCREENSHOTS
n/a
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Pei G, Hu R, Jia P, Zhao Z.
DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.
Nucleic Acids Res. 2021 Jul 2;49(W1):W131-W139. doi: 10.1093/nar/gkab429. PMID: 34048560; PMCID: PMC8262726.
:: DESCRIPTION
DeepARG is a machine learning solution that uses deep learning to characterize and annotate antibiotic resistance genes in metagenomes. It is composed of two models for two types of input: short sequence reads and gene-like sequences.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L.
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.
Microbiome. 2018 Feb 1;6(1):23. doi: 10.1186/s40168-018-0401-z. PMID: 29391044; PMCID: PMC5796597.
:: DESCRIPTION
DGEX is a deep learning method to infer the expression of target genes from the expression of landmark genes.
::DEVELOPER
CBCL Lab (Computational Biology and Computational Learning) @ UCI
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Gene expression inference with deep learning.
Chen Y, Li Y, Narayan R, Subramanian A, Xie X.
Bioinformatics. 2016 Feb 11. pii: btw074.
:: DESCRIPTION
DeepSEA is a deep learning-based algorithmic framework for predicting the chromatin effects of sequence alterations with single nucleotide sensitivity. DeepSEA can accurately predict the epigenetic state of a sequence, including transcription factors binding, DNase I sensitivities and histone marks in multiple cell types, and further utilize this capability to predict the chromatin effects of sequence variants and prioritize regulatory variants.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Zhou J, Troyanskaya OG.
Predicting effects of noncoding variants with deep learning-based sequence model.
Nat Methods. 2015 Oct;12(10):931-4. doi: 10.1038/nmeth.3547. Epub 2015 Aug 24. PMID: 26301843; PMCID: PMC4768299.
:: DESCRIPTION
DeepMod is a computational tool which takes long-read signals as input and outputs modification summary for each genomic position in a reference genome together with modification prediction for each base in a long read.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Liu Q, Fang L, Yu G, Wang D, Xiao CL, Wang K.
Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.
Nat Commun. 2019 Jun 4;10(1):2449. doi: 10.1038/s41467-019-10168-2. PMID: 31164644; PMCID: PMC6547721.
:: DESCRIPTION
Dove is a deep learning based protein docking model evluation method.It will use the atom information such as postions, types, energy scores in the interface area to judge if the docking model is reasonable.
::DEVELOPER
Kihara Bioinformatics Laboratory
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bioinformatics. 2019 Nov 20. pii: btz870. doi: 10.1093/bioinformatics/btz870. [Epub ahead of print]
Protein Docking Model Evaluation by 3D Deep Convolutional Neural Networks.
Wang X, Terashi G, Christoffer CW, Zhu M, Kihara D.
:: DESCRIPTION
DeepSynergy is a Deep Learning model for the prediction of synergy scores. It was trained on more than 22,000 samples based on 39 different cancer cell lines and combinations of 38 different drugs. DeepSynegy significantly outperformed other state-of-the-art machine learning methods.
::DEVELOPER
Institute of Bioinformatics, Johannes Kepler University Linz
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.
Preuer K, Lewis RPI, Hochreiter S, Bender A, Bulusu KC, Klambauer G.
Bioinformatics. 2018 May 1;34(9):1538-1546. doi: 10.1093/bioinformatics/btx806.
