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References from Protein subcellular localization prediction tools. Local targets link to admitted publications; unresolved targets remain external evidence.
Protein secondary structure prediction (pssp) using different machine algorithms
10.1186/s43042-021-00173-w · 2021 · External reference
10.1016/j.csbj.2019.12.011 · 2020 · Admitted local publication
Deep learning in protein structural modeling and design
2020 · External reference
Deep learning- based advances in protein structure prediction
10.3390/ijms22115553 · 2021 · External reference
End-to-end deep learning model to predict and design secondary structure content of structural proteins
10.1021/acsbiomaterials.1c01343 · 2022 · External reference
10.1016/j.csbj.2022.03.003 · 2022 · Admitted local publication
Different methods, techniques and their limitations in protein structure prediction: a review
10.1016/j.pbiomolbio.2022.05.002 · 2022 · External reference
Protein language models and structure prediction: connection and progression
2022 · External reference
Protein function analysis through machine learning
10.3390/biom12091246 · 2022 · External reference
Recent applications of deep learning methods on evolution-and contact-based protein structure pre- diction
10.3390/ijms22116032 · 2021 · External reference
Unresolved reference
External reference
Alphafold2 and its applications in the fields of biology and medicine
10.1038/s41392-023-01381-z · 2023 · External reference
Improved prediction of protein- protein interactions using alphafold2
10.1038/s41467-022-28865-w · 2022 · External reference
Improved pro- tein structure prediction using potentials from deep learning
10.1038/s41586-019-1923-7 · 2020 · External reference
Sequence alignment using machine learning for accurate template-based protein structure prediction
10.1093/bioinformatics/btz483 · 2020 · External reference
10.1016/j.csbj.2022.11.058 · 2023 · Admitted local publication
Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and lda
2020 · External reference
ideepsubmito: iden- tification of protein submitochondrial localization with deep learning
2021 · External reference
Sclpred-ems: subcellular localization prediction of endomembrane system and secretory pathway proteins by deep n- to-1 convolutional neural networks
10.1093/bioinformatics/btaa156 · 2020 · External reference
E-mula: an ensemble multi-localized attention feature extraction network for viral protein subcellular localization
10.3390/info15030163 · 2024 · External reference
Protein subcellu- lar localization prediction model based on graph convolutional network
10.1007/s12539-022-00529-9 · 2022 · External reference
Predicting subcellular location of protein with evolution information and sequence-based deep learning
10.1186/s12859-021-04404-0 · 2021 · External reference
Unite and conquer: enhanced prediction of protein subcellular localization by integrating multiple specialized tools
2007 · External reference
Meta-prediction of protein subcellular localization with reduced voting
2007 · External reference
Sherloc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data
10.1093/bioinformatics/btm115 · 2007 · External reference
Target: a new method for predicting protein subcellular localization in eukaryotes
10.1093/bioinformatics/bti650 · 2005 · External reference
Eslpred: Svm-based method for subcellular localization of eukaryotic proteins using dipeptide composition and psi- blast
10.1093/nar/gkh350 · 2004 · External reference
Critical evaluation of web-based prediction tools for human protein subcellular localization
2019 · External reference
Protein subcellular localiza- tion prediction
10.1007/978-1-0716-1641-3_12 · 2021 · External reference
Bird eye view of protein subcellular localization prediction
10.3390/life10120347 · 2020 · External reference
10.1007/978-1-0716-1641-3_15
10.1007/978-1-0716-1641-3_15 · External reference
Recent advances in the prediction of subcellular localization of proteins and related topics
10.3389/fbinf.2022.910531 · 2022 · External reference
A comparison of mutual information, linear models and deep learning networks for protein secondary structure prediction
10.2174/1574893618666230417103346 · 2023 · External reference
A systematic review of state-of-the-art strategies for machine learning-based protein function prediction
2023 · External reference
10.1016/j.csbj.2019.12.011 · 2020 · Admitted local publication
10.1007/978-3-030-02634-9_10
10.1007/978-3-030-02634-9_10 · External reference
Structure-based protein design with deep learning
10.1016/j.cbpa.2021.08.004 · 2021 · External reference
Correct machine learning on protein sequences: a peer-reviewing perspective
10.1093/bib/bbv082 · 2016 · External reference
Contact map predic- tion by machine learning
2010 · External reference
Progress at protein structure prediction, as seen in casp15
10.1016/j.sbi.2023.102594 · 2023 · External reference
Critical evaluation of web-based prediction tools for human protein subcellular localization
10.1093/bib/bbz106 · 2020 · External reference
10.1016/j.csbj.2021.10.023 · 2021 · Admitted local publication
ictx-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels
10.1155/2014/286419 · 2014 · External reference
Exten- sive feature detection of n-terminal protein sorting signals
10.1093/bioinformatics/18.2.298 · 2002 · External reference
Predsl: a tool for the n-terminal sequence-based prediction of protein subcellular localization
10.1016/s1672-0229(06)60016-8 · 2006 · External reference
Finding nuclear localization signals
10.1093/embo-reports/kvd092 · 2000 · External reference
Prediction of human protein subcellular localization using deep learning
10.1016/j.jpdc.2017.08.009 · 2018 · External reference
Unresolved reference
2005 · External reference
Going from where to why—interpretable prediction of protein subcellular localiza- tion
10.1093/bioinformatics/btq115 · 2010 · External reference
Predicting subcellular localization of proteins by bioinfor- matic algorithms
2017 · External reference
Persistent-homology-based machine learning: a survey and a comparative study
10.1007/s10462-022-10146-z · 2022 · External reference
A brief history of protein sorting prediction
10.1007/s10930-019-09838-3 · 2019 · External reference
Prediction of subcellular locations of proteins: where to proceed
10.1002/pmic.201000274 · 2010 · External reference
mlasso-hum: a lasso-based interpretable human-protein subcellular localization predictor
10.1016/j.jtbi.2015.06.042 · 2015 · External reference
A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mploc 2.0
10.1016/j.ab.2009.07.046 · 2009 · External reference
mgoasvm: multi-label protein subcellular localization based on gene ontology and support vector ma- chines
2012 · External reference
Hybridgo-loc: mining hybrid features on gene ontology for predicting subcellular localization of multi- location proteins
2014 · External reference
R3p-loc: a compact multi- label predictor using ridge regression and random projection for protein subcellular localization
10.1016/j.jtbi.2014.06.031 · 2014 · External reference
mplr-loc: an adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction
10.1016/j.ab.2014.10.014 · 2015 · External reference
Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites
2012 · External reference
10.1109/bibm.2013.6732715
10.1109/bibm.2013.6732715 · External reference
An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity
2012 · External reference
The linear neighborhood propagation method for predicting long non-coding rna–protein interac- tions
10.1016/j.neucom.2017.07.065 · 2018 · External reference
Subcons: a new ensemble method for improved human subcellular localization predictions
10.1093/bioinformatics/btx219 · 2017 · External reference
Multiloc2: integrating phylogeny and gene ontology terms improves subcellular protein local- ization prediction
10.1186/1471-2105-10-274 · 2009 · External reference
Sherloc2: a high-accuracy hybrid method for predicting subcellular localization of proteins
10.1021/pr900665y · 2009 · External reference
Prediction of protein subcellular localization
10.1002/prot.21018 · 2006 · External reference
Loctree2 predicts localization for all domains of life
10.1093/bioinformatics/bts390 · 2012 · External reference
10.1038/srep44598
10.1038/srep44598 · External reference
Plant-msubp: a computational framework for the prediction of single-and multi-target protein subcel- lular localization using integrated machine-learning approaches
2020 · External reference
Deeploc: prediction of protein subcellular localization using deep learning
10.1093/bioinformatics/btx431 · 2017 · External reference
Identifying protein subcellular locali- sation in scientific literature using bidirectional deep recurrent neural network
10.1038/s41598-020-80441-8 · 2021 · External reference
iloc-animal: a multi- label learning classifier for predicting subcellular localization of animal proteins
10.1039/c3mb25466f · 2013 · External reference
Mitochondrial genome annotation with mfannot: a critical analysis of gene identification and gene model prediction
10.3389/fpls.2023.1222186 · 2023 · External reference
Unified rational protein engineering with sequence-based deep represen- tation learning
10.1038/s41592-019-0598-1 · 2019 · External reference
Modeling aspects of the language of life through transfer-learning protein sequences
10.1186/s12859-019-3220-8 · 2019 · External reference
In-pero: ex- ploiting deep learning embeddings of protein sequences to predict the localisation of peroxisomal proteins
10.3390/ijms22126409 · 2021 · External reference
Prediction of lipoprotein signal peptides in gram-positive bacteria with a hidden markov model
10.1021/pr800162c · 2008 · External reference
Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of gram-positive bacteria
10.1007/s11274-008-9795-2 · 2008 · External reference
Metalocgramn: a meta- predictor of protein subcellular localization for gram-negative bacte- ria
10.1016/j.bbapap.2012.05.018 · 2012 · External reference
Psortb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes
10.1093/bioinformatics/btq249 · 2010 · External reference
Pslpred: prediction of subcellular localization of bacterial proteins
10.1093/bioinformatics/bti309 · 2005 · External reference
Sosui-gramn: high performance prediction for sub- cellular localization of proteins in gram-negative bacteria
10.6026/97320630002417 · 2008 · External reference
Protein sequence information extraction and subcellular localization prediction with gapped k-mer method
2019 · External reference
Signal peptides generated by attention-based neural networks
10.1021/acssynbio.0c00219 · 2020 · External reference
iloc-virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
10.1016/j.jtbi.2011.06.005 · 2011 · External reference
Virify: an integrated detection, annotation and taxonomic classification pipeline using virus- specific protein profile hidden markov models
10.1371/journal.pcbi.1011422 · 2023 · External reference
Busca: an integrative web server to predict subcellular localization of proteins
10.1093/nar/gky320 · 2018 · External reference
Sclpred: protein subcellu- lar localization prediction by n-to-1 neural networks
10.1093/bioinformatics/btr494 · 2011 · External reference
Scl-epred: a generalised de novo eukaryotic protein subcellular localisation predictor
10.1007/s00726-013-1491-3 · 2013 · External reference
Ml-fgat: Iden- tification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks
10.1016/j.compbiomed.2024.107944 · 2024 · External reference
Graph-bert and language model- based framework for protein–protein interaction identification
10.1038/s41598-023-31612-w · 2023 · External reference
Adappi: identification of novel protein functional modules via adaptive graph convolution networks in a protein–protein interaction network
10.1093/bib/bbac523 · 2023 · External reference
A novel hybrid cnn and bigru-attention based deep learning model for protein function prediction
10.1515/sagmb-2022-0057 · 2023 · External reference
Unresolved reference
2023 · External reference
Multi-scale deep learning for the imbalanced multi- label protein subcellular localization prediction based on immunohisto- chemistry images
10.1093/bioinformatics/btac123 · 2022 · External reference
Protein subcellular localization prediction by combining protbert and bigru
2022 · External reference
Signalp 6.0 predicts all five types of signal peptides using protein language models
10.1038/s41587-021-01156-3 · 2022 · External reference
Multiple protein subcellular locations prediction based on deep convolutional neural net- works with self-attention mechanism
10.1007/s12539-021-00496-7 · 2022 · External reference
10.1109/cibcb55180.2022.9863051
10.1109/cibcb55180.2022.9863051 · External reference
Protplat: an efficient pre-training platform for protein classification based on fasttext
10.1186/s12859-022-04604-2 · 2022 · External reference
Secproct: In silico prediction of human secretory proteins based on capsule network and transformer
2021 · External reference
10.1016/j.csbj.2021.08.027 · 2021 · Admitted local publication
Tripletprot: deep representation learning of proteins based on siamese networks
10.1109/tcbb.2021.3108718 · 2022 · External reference
Protein subcellular localization based on deep image features and criterion learning strategy
2021 · External reference
Prottrans: toward understanding the language of life through self- supervised learning
10.1109/tpami.2021.3095381 · 2022 · External reference
Prottrans: toward understanding the language of life through self-supervised learning
10.1109/tpami.2021.3095381 · 2021 · External reference
Psortdb 4.0: expanded and redesigned bacterial and archaeal protein subcellular localization database incorporating new secondary localizations
10.1093/nar/gkaa1095 · 2021 · External reference
Psortm: a bacterial and archaeal protein subcellular localization prediction tool for metagenomics data
10.1093/bioinformatics/btaa136 · 2020 · External reference
Imploc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images
10.1093/bioinformatics/btz909 · 2020 · External reference
Predictions of apop- tosis proteins by integrating different features based on improving pseudo-position-specific scoring matrix
2020 · External reference
Human protein subcellular localization identification via fuzzy model on kernelized neighborhood representa- tion
10.1016/j.asoc.2020.106596 · 2020 · External reference
Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization
10.1007/s11517-020-02275-w · 2020 · External reference
Gasphos: protein phosphorylation site prediction using a new feature selection approach with a ga-aided ant colony system
10.3390/ijms21217891 · 2020 · External reference
Deepmito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks
10.1093/bioinformatics/btz512 · 2020 · External reference
Ml-rbf: Predict protein subcellular locations in a multi-label system using evolutionary features
10.1016/j.chemolab.2020.104055 · 2020 · External reference
Ploc_deep-manimal: a novel deep cnn-blstm network to predict subcellular localization of animal proteins
2020 · External reference
Detecting sequence signals in targeting peptides using deep learning
2019 · External reference
Prediction of protein subcellular localization based on fusion of multi-view features
2019 · External reference
ploc-mgpos: incorporate key gene ontology information into general pseaac for predicting subcellular localization of gram-positive bacterial proteins
2017 · External reference
ploc-mvirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal go information into general pseaac
10.1016/j.gene.2017.07.036 · 2017 · External reference
10.1016/j.neucom.2015.09.137
10.1016/j.neucom.2015.09.137 · External reference
Sclpred- mem: subcellular localization prediction of membrane proteins by deep n-to-1 convolutional neural networks
10.1002/prot.26144 · 2021 · External reference
Sclpredt: Ab initio and homology-based prediction of subcellular localization by n-to-1 neural networks
10.1186/2193-1801-2-502 · 2013 · External reference
Toward the solution of the protein structure prediction problem
10.1016/j.jbc.2021.100870 · 2021 · External reference
Machine learning for protein folding and dynamics
10.1016/j.sbi.2019.12.005 · 2020 · External reference
Predicting the structure of large protein complexes using alphafold and monte carlo tree search
10.1038/s41467-022-33729-4 · 2022 · External reference
Deep learning in drug discovery: an integrative review and future challenges
10.1007/s10462-022-10306-1 · 2023 · External reference
Structure prediction in low dimensions: concepts, issues and examples
10.1098/rsta.2022.0246 · 2023 · External reference
A brief history of de novo protein design: minimal, rational, and computational
10.1016/j.jmb.2021.167160 · 2021 · External reference
How to fold graciously
1969 · External reference
Tools for the recognition of sorting signals and the prediction of subcellular localization of proteins from their amino acid sequences
2020 · External reference
Understudied proteins: opportunities and challenges for functional pro- teomics
10.1038/s41592-022-01454-x · 2022 · External reference
Current successes and remaining challenges in protein function prediction
10.3389/fbinf.2023.1222182 · 2023 · External reference
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
2022 · External reference
Deep learning techniques have significantly im- pacted protein structure prediction and protein design
10.1016/j.sbi.2021.01.007 · 2021 · External reference
Large ai models in health informatics: applications, challenges, and the future
2023 · External reference
Bird eye view of protein subcellular localization prediction
10.3390/life10120347 · 2020 · External reference
30 years of repeat expansion disorders: what have we learned and what are the remaining challenges?
10.1016/j.ajhg.2021.03.011 · 2021 · External reference
Challenges in describing the conformation and dynamics of proteins with ambiguous behavior
10.3389/fmolb.2022.959956 · 2022 · External reference
