Machine Learning Based LncRNA Function Prediction

The bioinformatics research of lncRNA has attracted much attention in both academics and industry due to the important roles of gene expression in the genome. LncRNA expression profile has large number of dimensions and small number of samples. In the study of the cancer samples' expression profile, there are a mass of redundant lncRNAs which are unrelated to cancer classification. Selecting key lncRNAs which are closely related to the pathophysiology of cancer from massive lncRNAs, can not only find prognostic factors for survival in cancer patients, but also provide guidance for the target of the precise medical in the future and greatly reduce the cost of biological analysis. In this paper, a new key lncRNA prediction method based on BPSO and ELM is proposed, lncRNAs' combination optimization problem is transformed into a binary particle swarm optimization model. The classification accuracy of ELM is used as the evaluation criterion of feature selection in BPSO.