miRNA and gene expression based cancer classification using self-learning and co-training approaches

A number of attempts to classify cancer samples using miRNA/gene expression profiles are known in literature. However, semi-supervised learning models have only been recently introduced to exploit the huge unlabeled expression profiles in enhancing sample classification. It is important to combine both miRNA and gene expression sets as that provides more information on the characteristics of cancer samples. The use of both of labeled and unlabeled miRNA and gene expression sets to enhance sample classification has not been explored yet. In this paper, two semi-supervised machine learning approaches, namely self-learning and co-training are adapted to enhance the quality of cancer sample classification. In self-learning, miRNA and gene based classifiers are enhanced independently. While in co-training, both miRNA and gene expression profiles are used simultaneously to provide different views of cancer samples. The approaches were evaluated using breast cancer, hepatocellular carcinoma (HCC) and lung cancer expression sets. Results show up to 20% improvement in F1-measure over Random Forests and SVM classifiers. Co-Training also outperforms Low Density Separation (LDS) approach by around 25% improvement in F1-measure in breast cancer.