SC⁃BPSO: A Filter Fused BPSO Feature Selection Method in Hepatocellular Carcinoma Classification / 中国生物化学与分子生物学报

ABSTRACT

Early diagnosis of cancer can significantly improve the survival rate of cancer patients, especially in patients with hepatocellular carcinoma (HCC). Machine learning is an effective tool in cancer classification. How to select high⁃classification accuracy feature subsets with low dimension in complex and high⁃dimensional cancer datasets is a difficult problem in cancer classification. In this paper, we propose a novel feature selection method, SC⁃BPSO a two⁃stage feature selection method implemented by combining the Spearman correlation coefficient, chi⁃square independent test⁃based filter method, and binary particle swarm optimal (BPSO) based wrapper method. It has been applied to the cancer classification of high⁃dimensional data to classify normal samples and HCC samples. The dataset in this paper is obtained from 130 liver tissue microRNA sequence data (64 hepatocellular carcinoma, 66 normal liver tissue) from National Center for Bioinformatics (NCBI) and European Bioinformatics Institute (EBI). First, the liver tissue microRNA sequence data was preprocessed to extract the three types of features of microRNA expression, editing level and post⁃editing expression. Then, the parameters of the SC⁃BPSO algorithm in the liver cancer classification were adjusted to select a subset of key features. Finally, classifiers were used to establish classification models, predict the results, and compare the classification results with the feature subset selected by the information gain filter, the information gain ratio filter and the BPSO wrapper feature selection algorithm using the same classifier. Using the feature subset selected by the SC⁃BPSO algorithm, the classification accuracy is up to 98. 4%. The experimental results showed that compared with the other three feature selection algorithms, the SC⁃ BPSO algorithm can effectively find feature subsets with relatively small size and higher accuracy. This may have important implications for cancer classification with a small number of samples and high⁃ dimension features.