The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data
Abstract
With the development of artificial intelligence (AI) technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning. This review focuses on computational approaches and tools for the prediction of protein – DNA/RNA interactions using machine intelligence techniques. We provide an overview of the development progress of computational methods and summarize the advantages and shortcomings of these methods. We further compiled applications in tasks related to the protein – DNA/RNA interactions, and pointed out possible future application trends. Moreover, biological sequence‐digitizing representation strategies used in different types of computational methods are also summarized and discussed.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Rapid advances in artificial intelligence and machine learning are already enabling substantial advances in biological design that were unimaginable 5 years ago (193–196), and the application of these techniques to the PFAS problem has already started to appear in the scientific literature (197–199).”
From Toward the development of a molecular toolkit for the microbial remediation of per-and polyfluoroalkyl substances (2024) · cites this paper as [194]
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1×
“Recently, free access to the AlphaFold protein structure database [42] (developed by DeepMind and EMBL-EBI) has been provided.”
MethodUnified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131
Evaluating Protein Transfer Learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719
Modeling the language of life – Deep Learning Protein Sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606
Kulmanov, M., Khan, M. A., & Hoehndorf, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx624 · Cited by 558
Research works printed but not linked: 82
- Sagendorf J. M., 2020, DNAproDB: An expanded database and web‐based tool for structural analysis of DNA‐protein complexes, Nucleic Acids Research, 48, D277
- 10.1093/nar/gkx407 · DOI
- 10.1038/nrm.2017.56 · DOI
- 10.1016/j.cell.2013.01.003 · DOI
- 10.1038/s41564-020-00846-z · DOI · Paper page
- 10.1016/j.compbiomed.2021.105092 · DOI
- 10.1006/jmbi.1999.2659 · DOI
- 10.1093/nar/gkg386 · DOI
- 10.1002/(SICI)1097-0134(19990401)35:1<114::AID-PROT11>3.0.CO;2-T · DOI
- 10.1093/nar/gkv020 · DOI
- 10.1093/bioinformatics/bty653 · DOI
- 10.1093/bib/bbaa270 · DOI
- 10.1093/bib/bbab089 · DOI
- 10.1093/bioinformatics/btv696 · DOI
- 10.1093/bioinformatics/btx614 · DOI
- 10.1093/bioinformatics/btv666 · DOI
- 10.1093/nar/gkh435 · DOI
- Boutet E., 2016, UniProtKB/Swiss‐Prot, the manually annotated section of the UniProt KnowledgeBase: How to use the entry view Plant Bioinformatics, 23
- 10.1038/75556 · DOI · Paper page
- 10.1093/nar/gkaa913 · DOI · Paper page
- Sigrist C. J. A., 2013, New and continuing developments at PROSITE, Nucleic Acids Research, 41, D344, 10.1093/nar/gks1067 · DOI
- 10.1016/j.ygeno.2020.08.016 · DOI
- 10.1186/s12967-021-03084-x · DOI
- 10.1007/s40192-018-0117-8 · DOI · Paper page
- 10.1109/JBHI.2016.2636665 · DOI · Paper page
- 10.1093/bib/bbab105 · DOI
- 10.1093/bib/bbaa314 · DOI
- 10.1038/nbt.3300 · DOI · Paper page
- 10.1093/bib/bbab008 · DOI
- 10.1093/bioinformatics/btx431 · DOI
- 10.1093/bioinformatics/bty573 · DOI
- Sutton R. S., 2018, Reinforcement learning: An introduction
- 10.1093/bioinformatics/btl672 · DOI
- 10.1186/1471-2105-14-90 · DOI
- 10.1093/nar/gkq474 · DOI
- 10.5858/arpa.2016-0471-ED · DOI · Paper page
- Gupta S. Agrawal A. Gopalakrishnan K. &Narayanan P.(2015).Deep learning with limited numerical precision.Paper presented at the International conference on machine learning.
- Pedregosa F., 2011, Scikit‐learn: Machine learning in Python, The Journal of Machine Learning Research, 12, 2825
- Ketkar N., 2017, Introduction to keras Deep learning with Python, 97, 10.1007/978-1-4842-2766-4_7 · DOI
- Paszke A., 2019, Pytorch: An imperative style, high‐performance deep learning library, Advances in Neural Information Processing Systems, 32, 8026
- Abadi M. Barham P. Chen J. Chen Z. Davis A. Dean J. Devin M. Ghemawat S. Irving G. Isard M. Kudlur M. Levenberg J. Monga R. Moore S. Murray D. G. Steiner B. Tucker P. Vasudevan V. Warden P. Wicke M. Yu Y.&Zheng X.… (2016). Tensorflow: A system for large‐scale machine learning. Paper presented at the 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16).
- Bastien F. Lamblin P. Pascanu R. Bergstra J. Goodfellow I. Bergeron A. Bouchard N. Warde‐Farley D.&Bengio Y.… (2012). Theano: New features and speed improvements. arXiv preprint arXiv:1211.5590. · arXiv
- 10.1145/1656274.1656278 · DOI · Paper page
- 10.1093/bfgp/elaa023 · DOI
- 10.1109/ACCESS.2021.3060612 · DOI
- 10.1093/bioinformatics/btq112 · DOI
- 10.1093/nar/gkm391 · DOI
- 10.1093/bfgp/elaa030 · DOI
- 10.1093/bioinformatics/btaa667 · DOI
- 10.1093/bioinformatics/btz682 · DOI · Paper page
- 10.1038/s41586-019-1923-7 · DOI · Paper page
- 10.1038/s41467-021-21511-x · DOI · Paper page
- 10.1038/s42256-021-00348-5 · DOI
- 10.1186/1471-2105-12-489 · DOI
- 10.1093/nar/gkl298 · DOI
- 10.1093/bioinformatics/btl672 · DOI
- 10.1093/bioinformatics/btm174 · DOI
- 10.1093/nar/gkz203 · DOI
- Hassanzadeh H. R. &Wang M. D.(2016 December 15–18).DeeperBind: Enhancing prediction of sequence specificities of DNA binding proteins.Paper presented at the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). · DOI
- 10.1093/nar/gkw226 · DOI
- 10.1186/s12864-018-4889-1 · DOI
- 10.1186/s12859-020-03952-1 · DOI
- Nguyen B. P., 2019, iProDNA‐CapsNet: Identifying protein‐DNA binding residues using capsule neural networks, BMC Bioinformatics [Electronic Resource], 20, 1
- 10.1093/nar/gkq061 · DOI
- 10.1186/1471-2164-14-651 · DOI
- 10.1093/nar/gkv585 · DOI
- 10.1093/nar/gkl544 · DOI
- 10.1371/journal.pcbi.1000832 · DOI
- 10.1093/nar/gkt890 · DOI
- 10.1186/gb-2014-15-1-r17 · DOI
- 10.1093/bioinformatics/btg432 · DOI
- 10.1093/nar/gkm294 · DOI
- 10.1093/bioinformatics/btn583 · DOI
- 10.1093/nar/gkt421 · DOI
- Yan J., 2017, DRNApred, fast sequence‐based method that accurately predicts and discriminates DNA‐and RNA‐binding residues, Nucleic Acids Research, 45, e84
- 10.1093/bioinformatics/btw255 · DOI
- 10.1016/j.omtn.2018.03.001 · DOI
- 10.1109/TCBB.2018.2874267 · DOI
- 10.1093/nar/gky215 · DOI
- 10.1038/s41467-019-12920-0 · DOI
- 10.1093/nar/gkaa009 · DOI
- 10.1093/nar/gkv1025 · DOI
Cited by
Sorted by citations
Hu, M., & Scott, C. (2024) · Applied and Environmental Microbiology · doi:10.1128/aem.00157-24 · Cited by 63
Metadata
- Authors
- Cui, F., Zhang, Z., Cao, C., Zou, Q., Chen, D., & Su, X.
- Affiliations
- Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China
- Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou China
- College of Electrical and Information Engineering Quzhou University Quzhou China
- Foshan Maternal and Child Health Hospital Foshan Guangdong China
- ORCID
- Zou, Quan
- Venue
- PROTEOMICS
- Publisher
- Wiley
- Published
- 2022-02-13
- Volume
- 22
- Issue
- 8
- ISSN
- 1615-9853
- 1615-9861
- Cited by
- 40
- References
- 9
- Language
- en
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data. https://app.cochat.ai/papers/10.1002/pmic.202100197
- The paper
- Cui, F., Zhang, Z., Cao, C., Zou, Q., Chen, D., & Su, X. (2022). Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data. PROTEOMICS. https://doi.org/10.1002/pmic.202100197