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References from Identification of plant vacuole proteins by exploiting deep representation learning features. Local targets link to admitted publications; unresolved targets remain external evidence.
Molecular composition of plant vacuoles: important but less understood regulations and roles of tonoplast lipids
2015 · External reference
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Vacuole biogenesis in plants: how many vacuoles, how many models?
10.1016/j.tplants.2020.01.008 · 2020 · External reference
Plant vacuole morphology and vacuolar trafficking. Frontiers
2014 · External reference
Regulation of transport processes across the tonoplast. Frontiers
2014 · External reference
The yeast vacuolar membrane proteome
10.1074/mcp.m800372-mcp200 · 2009 · External reference
Vacuolar sulfate transporters are essential determinants controlling internal distribution of sulfate in Arabidopsis
10.1105/tpc.104.023960 · 2004 · External reference
10.1146/annurev-arplant-042811-105608
10.1146/annurev-arplant-042811-105608 · External reference
Vacuolar transporters and their essential role in plant metabolism
10.1093/jxb/erl183 · 2007 · External reference
pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC
10.1039/c7mb00267j · 2017 · External reference
Plant-mSubP: a computational framework for the prediction of single- and multi-target protein subcellular localization using integrated machine-learning approaches
2019 · External reference
MD-LBP: An efficient computational model for protein subcellular localization from HeLa cell lines using SVM
10.2174/1574893614666190723120716 · 2020 · External reference
VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques
10.1007/s12038-020-00076-9 · 2020 · External reference
Anticancer peptides prediction with deep representation learning features
2021 · External reference
Identification of sub-Golgi protein localization by use of deep representation learning features
10.1093/bioinformatics/btaa1074 · 2020 · External reference
Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins
10.3390/ijms22126409 · 2021 · External reference
10.1093/bfgp/elaa030
10.1093/bfgp/elaa030 · External reference
Predicting protein phosphorylation sites based on deep learning
10.2174/1574893614666190902154332 · 2020 · External reference
Review of the applications of deep learning in bioinformatics
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UMAP: uniform manifold approximation and projection for dimension reduction
10.21105/joss.00861 · 2018 · External reference
10.1093/bioinformatics/btq003
10.1093/bioinformatics/btq003 · External reference
Sequence clustering in bioinformatics: an empirical study
2020 · External reference
Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL)
10.1093/pcp/pcv170 · 2016 · External reference
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches
2019 · External reference
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences
10.1093/bioinformatics/bty140 · 2018 · External reference
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
10.1093/bib/bbz041 · 2020 · External reference
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks
2021 · External reference
Unified rational protein engineering with sequence-based deep representation learning
10.1038/s41592-019-0598-1 · 2019 · External reference
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10.1101/676825
10.1101/676825 · External reference
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2020 · External reference
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A novel features ranking metric with application to scalable visual and bioinformatics data classification
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10.1016/j.inffus.2018.11.019 · External reference
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation
10.1093/bioinformatics/bty1047 · 2019 · External reference
Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set
10.1109/tcbb.2013.146 · 2014 · External reference
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10.1093/bib/bbaa159
10.1093/bib/bbaa159 · External reference
DeepAVP: a dual-channel deep neural network for identifying variable-length antiviral peptides
10.1109/jbhi.2020.2977091 · 2020 · External reference
