  miRNA and gene expression profiles have been proved useful for classifying
cancer samples. Efficient classifiers have been recently sought and developed.
A number of attempts to classify cancer samples using miRNA/gene expression
profiles are known in literature. However, the use of semi-supervised learning
models have been used recently in bioinformatics, to exploit the huge corpuses
of publicly available sets. Using both labeled and unlabeled sets to train
sample classifiers, have not been previously considered when gene and miRNA
expression sets are used. Moreover, there is a motivation to integrate both
miRNA and gene expression for a semi-supervised cancer classification as that
provides more information on the characteristics of cancer samples. 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. These approaches exploit the huge public corpuses to enrich the
training data. 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. To our
knowledge, it is the first attempt to apply these learning approaches to cancer
classification. 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.
