Predicting protein function from protein/protein interaction data: a probabilistic approach

By Stanley Letovsky, Simon Kasif. Intelligent Systems in Molecular Biology, 2003

383 citations recorded by the source corpus.

Subfields: Cell Biology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/351eb4349b706761ccabd621c5155c80d703c783

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Citations in the corpus

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction (2013, methodology): “However, the above approaches [29,30,45-47] do not consider the hierarchy of categories.”
Protein Molecular Function Prediction by Bayesian Phylogenomics (2005, neutral): “expression and interaction data [21–26].”
Uncovering Biological Network Function via Graphlet Degree Signatures (2008, background): “Deﬁning the relationship between the PPI network topology and biological function and inferring protein function from it is one of the major challenges in”
Protein interaction networks from yeast to human. (2004, background): “Implicitly, modules are also the basis of approaches that functionally classify proteins according to their network neighbors [64–66].”
Collective Classification in Network Data (2008, background): “Yet, protein function is sometimes correlated with interacting proteins; so, given a set of labeled proteins, one can reason about the remaining labels using collective methods [23].”
Predicting protein functions with message passing algorithms (2004, methodology): “, 2001), the Bayesian approaches (Deng et al., 2002; Letovsky and Kasif, 2003), the Redundancy Method (Samanta and Liang, 2003) and a more recent Monte Carlo simulated annealing (SA) approach (Vazquez et al.”
Exploiting ontology graph for predicting sparsely annotated gene function (2015, background): “To this end, a variety of graph-theoretic and machine learning algorithms (Karaoz et al ., 2004; Letovsky and Kasif, 2003; Murali et al ., 2006; Sefer and Kingsford, 2011) have been developed to provide a way of refining and enhancing existing functional annotations [e.g. Gene Ontology (GO)…”
Graph theory and networks in Biology. (2006, methodology): “A related probabilistic approach to using interaction network topology to predict protein function has also been presented in [191].”