DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions

By Ioannis Xenarios, Lukasz Salwínski, Xiaoqun Joyce Duan, Patrick Higney, Sul-Min Kim, David S. Eisenberg. Nucleic Acids Res., 2002

1,774 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6b092ee6d04b4fa76a0452cf0696086a7c04644d

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Citations in the corpus

Kernel methods for predicting protein-protein interactions (2005, methodology): “This constrained selection method was tested with the spectrum and motif kernels using both the BIND interaction data and a set of trusted interactions similar to the one used by Martin et al. (2005) extracted from DIP and MIPS (Mewes et al., 2000; Xenarios et al., 2002).”
Detection of functional modules from protein interaction networks (2003, background): “yeast subset of the Database of Interacting Proteins (DIP) (Jan 2002 release).(18) The DIP database is appropriate”
Human protein interaction networks across tissues and diseases (2015, methodology): “Data of protein interactions were extracted using MyProteinNet (Basha et al., 2015) from BioGrid (Chatr-Aryamontri et al., 2015), DIP (Xenarios et al., 2002), IntAct (Kerrien et al., 2012), and MINT (Licata et al., 2012) databases.”
Analyzing yeast protein–protein interaction data obtained from different sources (2002, background): “Considering these constraints, it is important concurrently to develop computational systems, such as BIN”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, background): “…such as KEGG GENES [9]; (2) protein– protein interaction (PPI) network-based similarity can be derived from the distance between two targets in the PPI network [46–50] and (3) Gene Ontology (GO) semantic similarity can be computed using GO annotations [51] in some databases such as UniProt [52].”
Predicting protein-protein interactions using signature products (2005, methodology): “(2003) and the Database of Interacting Proteins (DIP) (Xenarios et al., 2002); the human, mouse and E.”
The Database of Interacting Proteins: 2004 update (2004, background): “Since our previous NAR report was published (8), the number of distinct binary protein±protein interactions has nearly doubled and, as of September 2003, exceeds 18 500.”
BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, background): “Consequently, public databases for human-specific molecular interaction data have been undergoing a rapid growth within the past decade, such as BIND [18], DIP [19], STITCH [20], HPRD [21], TTD [22], DrugBank [23], ChEMBL [24], KEGG [25], BindingDB [26], SuperTarget and Matador [27], to name a few.”