Improved Classification Method for Detecting Potential Interactions Between Genes
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.
KeywordsClassification 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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