Computational approaches for predicting protein-protein interactions: a survey.
Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein-protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein-protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein-protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein-protein interactions, explaining their assumptions, main ideas, and limitations.
Department of Computer Science, Wayne State University Detroit, Michigan, USA. jingkai@wayne.edu
Jingkai
J
Yu
Farshad
F
Fotouhi
D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D020798:Two-Hybrid System Techniques; D014481:United States