Prediction of Protein Function Using Protein–Protein Interaction Data

By Minghua Deng, Kui Zhang, Shipra Mehta, Ting Chen, Fengzhu Sun. Proceedings. IEEE Computer Society Bioinformatics Conference, 2002

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Network enrichment analysis: extension of gene-set enrichment analysis to gene networks (2012, background): “A few examples of current applications include: inference concerning the functions of previously unannotated genes [4], extension of GO terms and pathways [5], finding novel disease genes [6] and prioritization of disease gene candidates [7].”
Network-based prediction of protein function (2007, methodology): “For such a homogeneous second-order MRF, we have Deng et al (2003) treat one function at a time.”
Interactome: gateway into systems biology. (2005, background): “Efforts to assess biological relevance and/or reliability of HT-Y2H and HT-AP–MS interactome data sets have utilized phylogenetic conservation, subcellular localization patterns, co-expression correlations, and network topology (94–96,98,101,102,108,116–118).”
Identifying protein interaction subnetworks by a bagging Markov random field-based method (2012, positive): “Note that an MRF model has been applied for network-based analysis to predict protein function using PPI data and has achieved some degree of success (20,21).”
Network propagation: a universal amplifier of genetic associations (2017, background): “This early success of network propagation and related approaches 40–42 led to the development of numerous propagation-based algorithms for protein function prediction (TABLE 1) .”