Towards more realistic machine learning techniques for prediction of disease-associated genes

Many learning techniques have been applied to identify disease-associated genes. At the early, they usually approached this problem as a binary classification, where training set is comprised of positive and negative samples. In which, positive samples are constructed from known disease genes, whereas negative samples are the remaining which are not known to be associated with diseases. This is the limitation of the binary classification-based solutions, since the negative training set should be actual non-disease genes; however, construction of this set is nearly impossible in biomedical researches. Therefore, to reduce this uncertainty, more realistic classification-based methods have been proposed. For instance, unary classification technique based on one-class SVM method was proposed by learning from only positive samples. In addition, the remaining set may contain unknown disease genes; therefore, semi-supervised methods such as binary semi-supervised and positive and unlabeled (PU) learning classifications have been proposed. In particular, PU learning methods, which learn from both known disease genes and the remaining genes, were shown to outperform others. In these studies, data sources are usually represented by vectorial form for binary classifiers, while they are in kernel matrices for unary and PU learning ones. The kernel-based data fusion is only suitable for data with different types and it seems unfair for the comparison based on different data representations. Therefore, in this study, we compared different classification techniques for the disease gene prediction based on vectorial representation of samples. The simulation result showed that the unary classification technique, which combines both density and class probability estimation strategies, achieved the best performance, whereas it is worst for the one-class SVM-based method. Interestingly, performance of the best binary classification technique is comparable with that of biased SVM-based PU learning and binary semi-supervised classification methods. And, they are all better than the multi-level SVM-based one.