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Y. Niu, D. Otasek and I. Jurisica, “Evaluation of Linguistic Features Useful in Extraction of Interactions from Pub-Med; Application to Annotating Known, High-Through-put and Predicted Interactions in I2D [J],” Bioinformatics, Vol. 26, No. 1, 2020, pp. 111-119.

has been cited by the following article:

• TITLE: Protein-Protein Interaction Extraction Based on Convex Combination Kernel Function

  AUTHORS: Peng Chen, Jianyi Guo, Zhengtao Yu, Sichao Wei, Feng Zhou, Xin Yan

  KEYWORDS: Protein-Protein Interaction; Support Vector Machine; Convex Combination Kernel Function

  JOURNAL NAME: Journal of Computer and Communications, Vol.1 No.5, November 8, 2013

  ABSTRACT: Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.