Protein function prediction method based on PPI network and machine learning
TANG Jiaqi1, WU Jingli1,2,3
1. School of Computer Science and Information Engineering, Guangxi Normal University, Guilin Guangxi 541001, China; 2. Guangxi Key Laboratory of Multi-Source Information Mining and Safety, Guangxi Normal University, Guilin Guangxi 541001, China; 3. Guangxi Regional Multi-Source Information Integration and Intelligent Processing Cooperation Innovation Center, Guilin Guangxi 541001, China
Abstract：Aiming at the problem that the prediction method of protein function based on the current Protein-Protein Interaction (PPI) network has low precision and is susceptible to data noise, a new machine learning protein function prediction method named HPMM (HC, PCA and MLP based Method) was proposed, which combined Hierarchical Clustering (HC), Principal Component Analysis (PCA) and Multi-layer Perception (MLP). HPMM took comprehensive consideration from macro and micro perspectives. It combined the information of protein families, domains and important sites into the vertex attributes of PPI networks to alleviate the effect from the data noise of networks. Firstly, the features of function modules and attribute principal components were extracted by using HC and PCA. Secondly, a mapping relationship between multi-feature and multi-function, used to predict protein functions, was constructed by training the MLP model. Three homo sapiens PPI networks, which were annotated by Molecular Functions (MF), Biological Processes (BP), and Cellular Components (CC) respectively, were adopted in the experiments. Comparisons were performed among the HPMM algorithm, the Cosine Iterative Algorithm (CIA) and the Diffusing GO Terms in the Directed PPI Network (GoDIN) Algorithm. The experimental results indicate that HPMM can obtain higher precision and F-measure than algorithms CIA and GoDIN, which are purely PPI network based methods.
唐家琪, 吴璟莉. 基于PPI网络与机器学习的蛋白质功能预测方法[J]. 计算机应用, 2018, 38(3): 722-727.
TANG Jiaqi, WU Jingli. Protein function prediction method based on PPI network and machine learning. Journal of Computer Applications, 2018, 38(3): 722-727.
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