The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The STRING database in 2025: protein networks with directionality of regulation
Open accessAbstract
Proteins cooperate, regulate and bind each other to achieve their functions. Understanding the complex network of their interactions is essential for a systems-level description of cellular processes. The STRING database compiles, scores and integrates protein–protein association information drawn from experimental assays, computational predictions and prior knowledge. Its goal is to create comprehensive and objective global networks that encompass both physical and functional interactions. Additionally, STRING provides supplementary tools such as network clustering and pathway enrichment analysis. The latest version, STRING 12.5, introduces a new ‘regulatory network’, for which it gathers evidence on the type and directionality of interactions using curated pathway databases and a fine-tuned language model parsing the literature. This update enables users to visualize and access three distinct network types—functional, physical and regulatory—separately, each applicable to distinct research needs. In addition, the pathway enrichment detection functionality has been updated, with better false discovery rate corrections, redundancy filtering and improved visual displays. The resource now also offers improved annotations of clustered networks and provides users with downloadable network embeddings, which facilitate the use of STRING networks in machine learning and allow cross-species transfer of protein information. The STRING database is available online at https://string-db.org/.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
The Molecular Signatures Database Hallmark Gene Set Collection
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Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1000 · Cited by 9,726
Self-citationKEGG for taxonomy-based analysis of pathways and genomes
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., & Ishiguro-Watanabe, M. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac963 · Cited by 5,833
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329
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TFXcan reveals transcription factor programs driving GWAS loci
Adeluwa, T., Salazar-Magaña, S., Sumner, S., Gona, S., Nyasimi, F., Carbonetto, P., Baca, S. C., Freedman, M., Gusev, A., Liu, B., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101398
Goldiș, C., Racoviceanu, R., Negrea-Ghiulai, R., Mioc, A., Prodea, A., Atyim, E., Maksimovic, T., Lukinich-Gruia, A. T., Pricop, M.-A., & Șoica, C. (2026) · Molecules · doi:10.3390/molecules31183337
BindPred: a framework for predicting protein–protein binding affinity from language model embeddings
Piao, H., Boorla, V. S., Santra, S., & Maranas, C. D. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag309
Metadata
- Authors
- Szklarczyk, D., Nastou, K., Koutrouli, M., Kirsch, R., Mehryary, F., Hachilif, R., Hu, D., Peluso, M. E., Huang, Q., Fang, T., Doncheva, N. T., Pyysalo, S., Bork, P., Jensen, L. J., & von Mering, C.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-11-18
- Volume
- 53
- Issue
- D1
- Pages
- D730-D737
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W4404511387
- Cited by
- 668
- References
- 5
- FWCI
- 77.61
- Type
- journal article
- Language
- en
- Open access
- gold
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