Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 16-20, 25.doi: 10.11896/j.issn.1002-137X.2016.11A.004

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Efficient Prediction Method of Essential Proteins Based on PPI Network

HONG Hai-yan and LIU Wei   

  • Online:2018-12-01 Published:2018-12-01

Abstract: The essential protein is indispensable for cell life,so it is very helpful for us to understand the minimum requirements of cellular life and design the drug through identifying essential protein.With the development of high-throughput technologies,more and more protein- protein interactions (PPI) data has been obtained,which makes it possible to study essential protein from the network level.At present there are already a number of computational methods proposed for essential proteins identification,but these methods do not solve the PPI data false positive issues.In addition,existing methods generally just consider the topology of the network not considering biological information of protein on the network,and is still relatively lacking.Protein for human life activities of cells not only related to the topology of the network,but also related with protein biological information on the network.To solve the above problems,this paper presented an efficient new method to predict essential protein called EPP (Essential Proteins Predict).The algorithm predicts essential proteins through computing the importance score of protein in the PPI network,the higher importance score of protein is, the protein is more likely to be essential.We take the importance of rank P% of the protein as essential protein.When computing the importance score of essential protein,we synthetically considered the semantic similarity and credibility factors.Our method has low complexity,and considers not only the topology of the network but also the biological meaning of protein itself.Experimental results show that,compared with other conventional methods,our method can identify more essential protein,and its statistical indicators is also higher than other methods.

Key words: Essential protein,GO,Semantic similarity

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