Online Predicted Human Interaction Database

By Kevin R. Brown, Igor Jurisica. Bioinform., 2005

620 citations recorded by the source corpus.

Subfields: Cell Biology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/14ed65f92e12909133c2514c836c8a317f68af47

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Citations in the corpus

Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues (2005, methodology): “Online predicted human interaction database (OPHID, obtained on April 26, 2005) was used for the analysis of human protein interaction (Brown and Jurisica, 2005).”
HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores (2012, background): “). PPIs can be evaluated by many experimental methodologies, which have hugely different degrees of confidence and different experimental set-ups. For instance, while yeast two hybrid (Y2H) identifies direct physical interactions between two proteins, mass spectrometry (MS) based datasets report com”
GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop (2010, neutral): “Contact: gary.bader@utoronto.ca; quaid.morris@utoronto.ca Received and revised on August 30, 2010; accepted on September”
iRefIndex: A consolidated protein interaction database with provenance (2008, methodology): “Our construction of a protein interaction index involved parsing PSI-MI files provided by nine interaction databases including BIND [22,23], BioGRID [24], DIP [25], HPRD [26,27], IntAct [28,29], MINT [30], MPact [31], MPPI [32] and OPHID [33].”
A scored human protein–protein interaction network to catalyze genomic interpretation (2017, neutral): “For each of the 15,189 PPI identified in draft-PPI, we computed three predictors: (1) Heavy to light ratio, (2) Jaccard similarity coefficient; and (3) Edge betweenness centrality.”
Global topological features of cancer proteins in the human interactome (2006, background): “…is based on the principle of orthologous interactions described by Matthews et al. (2001), which was subsequently implemented (Lehner and Fraser, 2004; Brown and Jurisica, 2005) and further extended with the development of a confidence scoring function for interactions (Jonsson et al., 2006).”
Protein-to-protein interactions: Technologies, databases, and algorithms (2010, methodology): “Algorithms and software tools for analyzing and managing PPI networks are discussed in depth.”
Studying protein^protein interaction networks: a systems view on diseases (2012, methodology): “Data are obtained from the i2d [52] and OMIM [53] databases [45].”
Characterising and Predicting Haploinsufficiency in the Human Genome (2010, positive): “Two interaction networks were used. One is a binary protein-protein interaction network integrated from a number of sources”
Dynamic modularity in protein interaction networks predicts breast cancer outcome (2009, background): “To investigate this possibility, we first identified proteins that have many interacting partners (so called 'hubs') in a network of protein-protein interactions curated from the literature and high-throughput source”
Evaluation of linguistic features useful in extraction of interactions from PubMed; Application to annotating known, high-throughput and predicted interactions in I2D (2009, background): “These interactions fall into three broad groups: (i) known interactions from PubMed; (ii) experimental interactions from high-throughput screens; and (iii) computationally predicted interactions inferred between species using homology (Brown and Jurisica, 2005).”
Biological network comparison using graphlet degree distribution (2007, neutral): “Finally, “HS”, “HG”, “HB”, “HH”, and “HM” stand for human PPI networks by 32, 29, from BIND (2), HPRD (26), and MINT (42), respectively (BIND, HPRD, and MINT data have been downloaded from OPHID (6) on February 10, 2006).”