BioGRID

BioGRID: A general repository for interaction datasets. Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.


References in zbMATH (referenced in 43 articles )

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  1. Jisung Yoon, Kai-Cheng Yang, Woo-Sung Jung, Yong-Yeol Ahn: Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs (2020) arXiv
  2. De la Cruz Cabrera, Omar; Matar, Mona; Reichel, Lothar: Analysis of directed networks via the matrix exponential (2019)
  3. Rasti, Saeid; Vogiatzis, Chrysafis: A survey of computational methods in protein-protein interaction networks (2019)
  4. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  5. Sondhi, Arjun; Shojaie, Ali: The reduced PC-algorithm: improved causal structure learning in large random networks (2019)
  6. Zhang, Zhaopeng; Ruan, Jishou; Gao, Jianzhao; Wu, Fang-Xiang: Predicting essential proteins from protein-protein interactions using order statistics (2019)
  7. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  8. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  9. Cohen, Nathann; Coudert, David; Ducoffe, Guillaume; Lancin, Aurélien: Applying clique-decomposition for computing Gromov hyperbolicity (2017)
  10. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  11. Wang, Beilun; Singh, Ritambhara; Qi, Yanjun: A constrained (\ell1) minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models (2017)
  12. Indhumathy, M.; Arumugam, S.; Baths, Veeky; Singh, Tarkeshwar: Graph theoretic concepts in the study of biological networks (2016)
  13. Lei Meng, Aaron Striegel, Tijana Milenkovic: IGLOO: Integrating global and local biological network alignment (2016) arXiv
  14. Olofsson, Peter; Livingstone, Kevin; Humphreys, Joshua; Steinman, Douglas: The probability of speciation on an interaction network with unequal substitution rates (2016)
  15. Strash, Darren: On the power of simple reductions for the maximum independent set problem (2016)
  16. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Graph centrality based prediction of cancer genes (2016)
  17. Žurauskienė, Justina; Kirk, Paul D. W.; Stumpf, Michael P. H.: A graph theoretical approach to data fusion (2016)
  18. Erdem, Esra; Oztok, Umut: Generating explanations for biomedical queries (2015)
  19. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  20. Yang, Lei; Wang, Jizhe; Wang, Huiping; Lv, Yingli; Zuo, Yongchun; Jiang, Wei: Analysis and identification of toxin targets by topological properties in protein-protein interaction network (2014)

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