DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier
Open accessAbstract
Motivation: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results: We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Availability and implementation: Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. Contact: robert.hoehndorf@kaust.edu.sa. Supplementary information: Supplementary data are available at Bioinformatics online.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DeepGO used features from protein sequences and interactions to predict protein function; the features were used to build a hierarchical neural network classifier based on GO, so that predictions and features could be refined at each level of the biological hierarchy, resulting in a model optimized for function prediction.
“DeepGO used features from protein sequences and interactions to predict protein function; the features were used to build a hierarchical neural network classifier based on GO, so that predictions and features could be refined at each level of the biological hierarchy, resulting in a model optimized for function prediction (Kulmanov et al., 2018).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Kulmanov et al., 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
(2010) · Cited by 14,134 · see citing works
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436 · see citing works
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,882 · see citing works
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works
A Neural Probabilistic Language Model
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Jaz, K., Hofmann, T., Poggio, T., & Shawe-Taylor, J. S. (2000) · Cited by 3,451 · see citing works
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873 · see citing works
A neural probabilistic language model
BengioYoshua, DucharmeRéjean, VincentPascal, & JanvinChristian (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944966 · Cited by 2,646 · see citing works
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · see citing works
Resnik, P. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.514 · Cited by 2,098 · see citing works
Huerta‐Cepas, J., Szklarczyk, D., Forslund, S. K., Cook, H., Heller, D., Walter, M. C., Rattei, T., Mende, D. R., Sunagawa, S., Kuhn, M., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1248 · Cited by 2,062 · see citing works
InterPro in 2017—beyond protein family and domain annotations
Finn, R. D., Attwood, T. K., Babbitt, P. C., Bateman, A. G., Peer Bork, B., Bridge, A. J., Chang, H.-Y., Dosztányi, Z., El-Gebali, S., Fraser, M., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1107 · Cited by 1,455 · see citing works
A global genetic interaction network maps a wiring diagram of cellular function
Costanzo, M., VanderSluis, B., Koch, E. N., Baryshnikova, A., Pons, C., Tan, G., Wang, W., Usaj, M., Hanchard, J., Lee, S. D., et al. (2016) · Science · doi:10.1126/science.aaf1420 · Cited by 1,240 · see citing works
Network‐based prediction of protein function
Sharan, R., Ulitsky, I., & Ron Shamir, A. (2007) · Molecular Systems Biology · doi:10.1038/msb4100129 · Cited by 1,077 · see citing works
A large-scale evaluation of computational protein function prediction
Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2340 · Cited by 1,028 · see citing works
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works
Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium
Gaudet, P., Livstone, M. S., Lewis, S., & Thomas, P. D. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr042 · Cited by 901 · see citing works
Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M., Bansal, P., Bridge, A., Poux, S., Bougueleret, L., & Xenarios, I. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3167-5_2 · Cited by 824 · see citing works
Learning distributed representations of concepts.
Hinton, G. E. (1989) · eScholarship (California Digital Library) · Cited by 815 · see citing works
Critical assessment of methods of protein structure prediction (CASP) — round x
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24452 · Cited by 673 · see citing works
Annotating the human genome with Disease Ontology
Osborne, J. D., Flatow, J. M., Holko, M., Lin, S. M., Kibbe, W. A., Zhu, L. J., Danila, M. I., Feng, G., & Chisholm, R. L. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-s1-s6 · Cited by 265 · see citing works
Amalric, M., Wang, L., Pica, P., Figueira, S. D., Sigman, M., & Dehaene, S. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005273 · Cited by 196 · see citing works
Neuro-symbolic representation learning on biological knowledge graphs
Alshahrani, M., Khan, M. A., Maddouri, O., Kinjo, A. R., Queralt-Rosiñach, N., & Hoehndorf, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx275 · Cited by 161 · see citing works
Self-citationSystematic Functional Annotation and Visualization of Biological Networks
Baryshnikova, A. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.014 · Cited by 148 · see citing works
Das, S., Lee, D., Sillitoe, I., Dawson, N. L., Lees, J. G., & Orengo, C. A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv398 · Cited by 146 · see citing works
Information-theoretic evaluation of predicted ontological annotations
Clark, W. T., & Radivojac, P. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt228 · Cited by 143 · see citing works
FFPred 3: feature-based function prediction for all Gene Ontology domains
Cozzetto, D., Minneci, F., Currant, H., & Jones, D. T. (2016) · Scientific Reports · doi:10.1038/srep31865 · Cited by 127 · see citing works
The anatomy of phenotype ontologies: principles, properties and applications
Gkoutos, G. V., Schofield, P. N., & Hoehndorf, R. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx035 · Cited by 113 · see citing works
Self-citationGoFDR: A sequence alignment based method for predicting protein functions
Gong, Q., Ning, W., & Tian, W. (2015) · Methods · doi:10.1016/j.ymeth.2015.08.009 · Cited by 84 · see citing works
HIERARCHICAL CLASSIFICATION OF GENE ONTOLOGY TERMS USING THE GOstruct METHOD
Sokolov, A., & Ben‐Hur, A. (2010) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720010004744 · Cited by 82 · see citing works
CombFunc: predicting protein function using heterogeneous data sources
Wass, M. N., Barton, G. R., & Sternberg, M. J. E. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks489 · Cited by 68 · see citing works
Genome-scale phylogenetic function annotation of large and diverse protein familiesPreprint
Engelhardt, B. E., Jordan, M. I., Srouji, J. R., et al. (2011) · Genome Research · doi:10.1101/gr.104687.109 · Cited by 60 · see citing works
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
Jiang, J., & McQuay, L. J. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.156 · Cited by 58 · see citing works
Structure-Based Function Prediction of Uncharacterized Protein Using Binding Sites Comparison
Konc, J., Hodošček, M., Ogrizek, M., Konc, J. T., & Janežič, D. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003341 · Cited by 53 · see citing works
Protein annotation from protein interaction networks and Gene Ontology
Nguyen, C., Gardiner, K., & Cios, K. J. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.04.010 · Cited by 31 · see citing works
Protein Function Prediction Based on Patterns in Biological Networks
Kıraç, M., & Özsoyoğlu, G. (2008) · Springer eBooks · doi:10.1007/978-3-540-78839-3_18 · Cited by 24 · see citing works
Roles for Text Mining in Protein Function Prediction
Verspoor, K. M. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-0709-0_6 · Cited by 18 · see citing works
New Approaches of Protein Function Prediction from Protein Interaction Networks
Hou, J. (2017) · Figshare · Cited by 17 · see citing works
Protein Function Prediction from Functional Connectivity
Hou, J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809814-1.00007-8 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675 · see citing works
Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401 · see citing works
Using deep learning to annotate the protein universe
Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01179-w · Cited by 332 · see citing works
Knowledge graphs and their applications in drug discovery
MacLean, F. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1910673 · Cited by 105 · see citing works
Mapping the multiscale structure of biological systems
Schaffer, L. V., & Ideker, T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.012 · Cited by 75 · see citing works
Deep learning methods for protein function prediction
Boadu, F., Lee, A., & Cheng, J. (2025) · PROTEOMICS · doi:10.1002/pmic.202300471 · Cited by 53 · see citing works
Incorporating biological structure into machine learning models in biomedicine
Crawford, J., & Greene, C. S. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.021 · Cited by 41 · see citing works
DeepGOA: Predicting Gene Ontology Annotations of Proteins via Graph Convolutional Network
Zhou, G., Wang, J., Zhang, X., & Yu, G. (2019) · 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983075 · Cited by 32 · see citing works
Decoding functional proteome information in model organisms using protein language models
Barrios-Núñez, I., Martínez-Redondo, G. I., Medina-Burgos, P., Cases, I., Fernández, R., & Rojas, A. M. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae078 · Cited by 13 · see citing works
Fan, K., & Zhang, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00807 · Cited by 7 · see citing works
- Authors
- Kulmanov, M., Khan, M. A., & Hoehndorf, R.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- Corresponding author
- Hoehndorf, Robert
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-10-03
- Volume
- 34
- Issue
- 4
- Pages
- 660-668
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC5860606
- OpenAlex
- W2615066396
- Cited by
- 558
- References
- 41
- Citations by year
- 2026: 53 · 2025: 85 · 2024: 71 · 2023: 61 · 2022: 79 · 2021: 68 · 2020: 72 · 2019: 48 · 2018: 19 · 2017: 2
- FWCI
- 15.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- King Abdullah University of Science and Technology (FCC/1/1976-08-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein function prediction, Gene Ontology, protein sequence features, protein-protein interaction network, deep learning
- MeSH terms
- Supervised Machine Learning, Animals, Bacteria, Humans, Proteins, Software, Computational Biology, Sequence Analysis, Protein, Eukaryota, Protein Interaction Maps, Gene Ontology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
