Please use this identifier to cite or link to this item: https://doi.org/10.1007/11691730_1
Title: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions
Authors: Chua, H.N.
Sung, W.-K. 
Wong, L. 
Issue Date: 2006
Source: Chua, H.N.,Sung, W.-K.,Wong, L. (2006). Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3916 LNBI : 1-. ScholarBank@NUS Repository. https://doi.org/10.1007/11691730_1
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. © springer- verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/42140
ISBN: 3540331042
ISSN: 03029743
DOI: 10.1007/11691730_1
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