Evolving Multiobjective Cancer Subtype Diagnosis From Cancer Gene Expression Data

Detection and diagnosis of cancer are especially essential for early prevention and effective treatments. Many studies have been proposed to tackle the subtype diagnosis problems with those data, which often suffer from low diagnostic ability and bad generalization. This article studies a multiobjective PSO-based hybrid algorithm (MOPSOHA) to optimize four objectives including the number of features, the accuracy, and two entropy-based measures: the relevance and the redundancy simultaneously, diagnosing the cancer data with high classification power and robustness. First, we propose a novel binary encoding strategy to choose informative gene subsets to optimize those objective functions. Second, a mutation operator is designed to enhance the exploration capability of the swarm. Finally, a local search method based on the best/1 mutation operator of differential evolutionary algorithm (DE) is employed to exploit the neighborhood area with sparse high-quality solutions since the base vector always approaches to some good promising areas. In order to demonstrate the effectiveness of MOPSOHA, it is tested on 41 cancer datasets including thirty-five cancer gene expression datasets and six independent disease datasets. Compared MOPSOHA with other state-of-the-art algorithms, the performance of MOPSOHA is superior to other algorithms in most of the benchmark datasets.

Automated summary

Detection and diagnosis of cancer are crucial for early prevention and treatment. This article introduces a multiobjective PSO-based hybrid algorithm (MOPSOHA) for optimizing feature selection and diagnosis of cancer data, which outperforms other algorithms in various cancer datasets. The effectiveness of MOPSOHA is demonstrated through its novel encoding strategy, mutation operator, and local search method.