Improved Classification Method for Detecting Potential Interactions Between Genes

  • Li-Yeh Chuang
  • Yu-Da LinEmail author
  • Cheng-Hong YangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)


Multifactor dimensionality reduction (MDR) constitutes a highly accurate classification algorithm for gene–gene interaction (GGI) identification. GGI detection quality is commonly assessed using the correct classification rate (CCR). Nevertheless, the CCR alone might not be suitable for assessing the detection of some GGIs due to various model preferences and disease complexities. Accordingly, we developed an MDR-based multiple-objective method that combines the CCR and chi-squared measures (called MDR–Cχ2) for GGI detection. In the proposed method, a Pareto set operation is executed to ensure the combination of the CCR and chi-squared measures in the MDR process for GGI detection. The most significant GGIs within the Pareto sets are determined using cross-validation consistency values. Herein, we report the MDR and MDR–Cχ2 detection success rates in a simulated environment and demonstrate that the proposed MDR–Cχ2 provides superior GGI detection success rates.


Classification Multifactor dimensionality reduction Multiple objective 



The Ministry of Science and Technology, R.O.C., partially supported this study (Grants 105-2221-E-151-053-MY2 and 106-2811-E-151-002).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Chemical EngineeringI-Shou UniversityKaohsiungTaiwan
  2. 2.Department of Electronic EngineeringNational Kaohsiung University of Science and TechnologyKaohsiungTaiwan
  3. 3.Graduate Institute of Clinical MedicineKaohsiung Medical UniversityKaohsiungTaiwan

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