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Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm
Author(s) -
Sun Shiwei,
Zhao Yi,
Jiao Yishan,
Yin Yifei,
Cai Lun,
Zhang Yong,
Lu Hongchao,
Chen Runsheng,
Bu Dongbo
Publication year - 2006
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2006.02.053
Subject(s) - robustness (evolution) , computer science , algorithm , protein function , function (biology) , protein function prediction , data mining , biology , genetics , gene
On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP‐core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO's improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.