Literature-curated protein interaction datasets

By Michael E. Cusick, Haiyuan Yu, A. Smolyar, K. Venkatesan, Anne-Ruxandra Carvunis, Nicolas Simonis, Jean‐François Rual, Heather Borick, P. Braun, Matija Dreze and 6 more. Nature Methods, 2009

316 citations recorded by the source corpus.

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a2df39c87a3385fd0126a01e38c5cb3d75990e96

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Citations in the corpus

HINT: High-quality protein interactomes and their applications in understanding human disease (2012, methodology): “There are two major methods to obtain a global map of binary interactions – literature-curation (LC) and high-throughput experiments (HT) [18].”
A travel guide to Cytoscape plugins (2012, background): “Even in an organism such as budding yeast, in which large-scale genetic and physical interaction experiments have been performed, complete network coverage has not yet been achieved 19 .”
The ConsensusPathDB interaction database: 2013 update (2012, background): “Reportedly, considerable fractions of the available protein–protein interaction data may result from experimental or literature mining errors (26,27).”
Network-based methods for human disease gene prediction. (2011, background): “Common problems for predicting disease genes based on networks are the existence of noise (false positives) in curated PPI databases and gene expression profiling experiments in addition to the bias towards well-studied disease genes [23, 70].”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, methodology): “Adapted from Cusick, 17 with permission. have recently been recognized as powerful and ubiquitous gene regulatory mechanisms.”
An empirical framework for binary interactome mapping (2008, unknown): “Systematic recuration of all publications thought to support these 188 protein pair”
A proteome-scale map of the human interactome network. (2014, background): “…extracted, two thirds were reported in only a single publication and detected by only a single method (Lit-BS pairs), thus potentially presenting higher rates of curation errors than binary pairs supported bymultiple pieces of evidence (Lit-BMpairs; Tables S1A, S1B, andS1C) (Cusick et al., 2009).”
Artificial intelligence in cancer target identification and drug discovery (2022, background): “Second, most algorithms only use the topological information of the biological network, but neglect the association between cell function or phenotypes and topological features (such as centrality-based algorithms that are discussed in Section 3.1.2).”