Please use this identifier to cite or link to this item:
|Title:||Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions||Authors:||Chua, H.N.
|Issue Date:||2006||Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 26, 2020
checked on Oct 25, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.