Abstract
Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that two separate forms of functional association accounts for such a phenomenon, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested to find out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction.
We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources; (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs well.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chua, H.N., Sung, WK., Wong, L. (2006). Exploiting Indirect Neighbours and Topological Weight to Predict Protein Function from Protein-Protein Interactions. In: Li, J., Yang, Q., Tan, AH. (eds) Data Mining for Biomedical Applications. BioDM 2006. Lecture Notes in Computer Science(), vol 3916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11691730_1
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DOI: https://doi.org/10.1007/11691730_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33104-9
Online ISBN: 978-3-540-33105-6
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