STRING

STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.


References in zbMATH (referenced in 34 articles )

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  1. Cowen, Lenore; Devkota, Kapil; Hu, Xiaozhe; Murphy, James M.; Wu, Kaiyi: Diffusion state distances: multitemporal analysis, fast algorithms, and applications to biological networks (2021)
  2. Janyasupab, Panisa; Suratanee, Apichat; Plaimas, Kitiporn: Network diffusion with centrality measures to identify disease-related genes (2021)
  3. Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
  4. Altuntas, Volkan; Gok, Murat; Kocal, Osman Hilmi: Response of Lyapunov exponents to diffusion state of biological networks (2020)
  5. Tabouy, Timothée; Barbillon, Pierre; Chiquet, Julien: Variational inference for stochastic block models from sampled data (2020)
  6. van Wieringen, Wessel N.; Stam, Koen A.; Peeters, Carel F. W.; van de Wiel, Mark A.: Updating of the Gaussian graphical model through targeted penalized estimation (2020)
  7. Wang, Rongquan; Wang, Caixia; Liu, Guixia: A novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes from dynamic and static PPI networks (2020)
  8. Zhang, Hong; Tong, Tiejun; Landers, John; Wu, Zheyang: TFisher: a powerful truncation and weighting procedure for combining (p)-values (2020)
  9. Angelopoulos, Nicos; Wielemaker, Jan: Advances in big data bio analytics (2019)
  10. Huang, Ping; Ge, Peng; Tian, Qing-Fen; Huang, Guo-Bao: Prediction of key transcription factors during skin regeneration by combining gene expression data and regulatory network information analysis (2019)
  11. Kocheturov, Anton; Pardalos, Panos M.; Karakitsiou, Athanasia: Massive datasets and machine learning for computational biomedicine: trends and challenges (2019)
  12. Rasti, Saeid; Vogiatzis, Chrysafis: A survey of computational methods in protein-protein interaction networks (2019)
  13. Vogiatzis, Chrysafis; Camur, Mustafa Can: Identification of essential proteins using induced stars in protein-protein interaction networks (2019)
  14. How, Javier J.; Navlakha, Saket: Evidence of Rentian scaling of functional modules in diverse biological networks (2018)
  15. Li, Zhenpeng; Shang, Changjing; Shen, Qiang: Inter-variable correlation prediction with fuzzy connected-triples (2018)
  16. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  17. Squartini, Tiziano; Caldarelli, Guido; Cimini, Giulio; Gabrielli, Andrea; Garlaschelli, Diego: Reconstruction methods for networks: the case of economic and financial systems (2018)
  18. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  19. Wang, Dingjie; Zou, Xiufen: A new centrality measure of nodes in multilayer networks under the framework of tensor computation (2018)
  20. Akhmedov, Murodzhon; LeNail, Alexander; Bertoni, Francesco; Kwee, Ivo; Fraenkel, Ernest; Montemanni, Roberto: A fast prize-collecting Steiner forest algorithm for functional analyses in biological networks (2017)

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