Open AccessProtein Function Prediction Based on Active Semi‐supervised Learning
Author(s) -
Wang Xuesong,
Cheng Yuhu,
Li Lijing
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.07.005
Subject(s) - computer science , machine learning , artificial intelligence , set (abstract data type) , adjacency matrix , protein function prediction , function (biology) , artificial neural network , supervised learning , semi supervised learning , process (computing) , protein function , biology , gene , theoretical computer science , biochemistry , genetics , graph , programming language , operating system
In our study, the active learning and semisupervised learning methods are comprehensively used for label delivery of proteins with known functions in Proteinprotein interaction (PPI) network so as to predict the functions of unknown proteins. Because the real PPI network is generally observed with overlapping protein nodes with multiple functions, the mislabeling of overlapping protein may result in accumulation of prediction errors. For this reason, prior to executing the label delivery process of semi‐supervised learning, the adjacency matrix is used to detect overlapping proteins. As the topological structure description of interactive relation between proteins, PPI network is observed with party hub protein nodes that play an important role, in co‐expression with its neighborhood. Therefore, to reduce the manual labeling cost, party hub proteins most beneficial for improvement of prediction accuracy are selected for class labeling and the labeled party hub proteins are added into the labeled sample set for semisupervised learning later. As the experimental results of real yeast PPI network show, the proposed algorithm can achieve high prediction accuracy with few labeled samples.