Nature-inspired multiobjective patient stratification from cancer gene expression data

Abstract

Stratifying personalized treatment for patients has been one of the main challenges for modern medicine. To solve this problem, various clustering algorithms have been proposed for patient stratification in both quantification and biological ways meaningfully. However, most of the existing clustering algorithms still suffer from many realistic algorithm limitations such as low diagnostic ability and bad generalization. Therefore, to address those restrictions, we propose a novel multiobjective spectral clustering algorithm based on decomposition. A population that consists of distance weight and two other indispensable parameters of the spectral clustering is optimized by the proposed algorithm. Two cluster validity indices are proposed to capture the characteristics of different datasets. To validate the effectiveness and efficiency of the proposed algorithm, we benchmark it on thirty-five real patient stratification datasets and six real-world medical datasets across thousands of comparisons with fifteen algorithms, including ten effective clustering methods and five state-of-the-art multiobjective algorithms. The experimental results indicate that the proposed algorithm performs better than other compared algorithms with high clustering ability for patient stratification. Moreover, extensive analysis of time complexity and parameters are performed to prove the robustness of the proposed algorithm from different perspectives.