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Sequence-Based Prediction of Protein-Protein Interactions Using Random Tree and Genetic Algorithm

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Intelligent Computing Technology (ICIC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7389))

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Abstract

Protein-protein interactions play important roles in the course of cell functions such as metabolic pathways and genetic information processing. There are many shortcomings of traditional experiments such as tediousness and laboriousness. The machine learning methods have been developed to predict PPIs, and preliminary results have demonstrated their feasibility. Here, we introduce a sequence-based random tree and GA to infer PPI. Experimental results on S.cerevisiae dataset from DIP show that our novel method performs well than rotation forest, with higher accuracy, sensitivity and precision. Most importantly, our method runs faster than rotation forest.

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Author information

Authors and Affiliations

  1. College of life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China

    Lei Zhang

  2. Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China

    Lei Zhang

Authors
  1. Lei Zhang
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    Changjun Jiang

  3. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona, 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  4. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhang, L. (2012). Sequence-Based Prediction of Protein-Protein Interactions Using Random Tree and Genetic Algorithm. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_43

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  • DOI: https://doi.org/10.1007/978-3-642-31588-6_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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