Research on the Tacit Knowledge Discovery Based on Two-mode Complex Network——Take mining Potential Drug Targets as an Example

Abstract: [Purpose/significance] This paper aims to extract the tacit knowledge from the massive literatures by constructing a two-mode complex network model. [Method/process] Through the NetworkX complex network toolkit, a two-mode complex network model was constructed based on the co-occurrence relationship of any two nodes. The direct relationship between nodes and nodes was extracted by weighting the co-occurrence relationship of nodes in the network model, calculating the topology information of the network and AP clustering. The most appropriate prediction algorithm was selected by using AUC method to evaluate the four link prediction algorithms, such as AA, JC, wAA and wJC. The tacit knowledge was predicted by the most appropriate prediction algorithm from the complex networks. [Result/conclusion] The results showed that the wAA link prediction algorithm was the optimal link prediction algorithm. The two mode complex network model, indicators and method system were effective in drug target mining in the Chemical Abstracts Service database. The next step is to try in other databases or other research fields to further verify the generality and effectiveness of the model.