STRING: a database of predicted functional associations between proteins

By C. von Mering, Martijn A. Huynen, Daniel Jaeggi, Steffen Schmidt, Peer Bork, Berend Snel. Nucleic Acids Res., 2003

2,618 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fa69e5e6d21650daa286ed936af9c53d1aca00df

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Citations in the corpus

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine (2023, methodology): “At the molecular level, relationships such as protein–protein interactions (PPI), signal and transcriptional regulation between molecules are mainly mined from databases such as STRING [31], KEGG [32] and BioGRID [33].”
The BioGRID Interaction Database: 2011 update (2010, background): “BioGRID data are also available through partner model organism databases (MODs) including SGD (8), FlyBase (21), TAIR (10) and GeneDB (9), through external resources such as NCBI Entrez-Gene, DroID (22) and GermOnline (23), and through meta-databases such as STRING (24), iRefIndex (25) and Pathway C”
Protein interaction networks from yeast to human. (2004, methodology): “33. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B: STRING: a database of predicted functional associations between proteins.”
The Proteomics Protocols Handbook (2005, methodology): “It is interesting to note that although the addition of thiourea to this mixture increases both the number and quality of proteins that are detected, these additional proteins are water-soluble proteins, not membrane proteins (56,57).”
Data Mining Techniques for the Life Sciences (2010, methodology): “Altschul, S. F., et al. (1997) Gapped BLAST and PSI-BLAST, a new generation of protein database search programs.”
Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps (2005, methodology): “This treats each r i as a probability and assumes independence; this approach is very similar to the one taken by von Mering et al. (2003a).”
Using the Gene Ontology Hierarchy when Predicting Gene Function (2009, methodology): “Results show that using the hierarchy information directly, compared to using reconciliation methods, improves gene function prediction.”
Walking the interactome for prioritization of candidate disease genes. (2008, methodology): “Additionally, text mining has been used as one of the components of STRING to predict protein-protein interactions.(27) Although these computational techniques increase the coverage of proteins and interactions, they presumably come at the cost of reducing the overall accuracy of the data by introdu”
Online Predicted Human Interaction Database (2005, neutral): “Several databases have also been published that make predictions about the functional relationships between proteins based on a variety of in silico methods (Predictome, STRING, Prolinks, POINT) (Bowers et al. , 2004; Huang et al. , 2004; Mellor et al. , 2002; von Mering et al. , 2003).”
Prolinks: a database of protein functional linkages derived from coevolution (2004, methodology): “Although this is not the first database that analyzes protein coevolution, it is in many respects distinct from existing tools [5,6].”