Title:Recent Advances in Machine Learning Methods for LncRNA-Cancer
Associations Prediction
Volume: 4
Issue: 3
Author(s): Ruobing Wang, Lingyu Meng and Jianjun Tan*
Affiliation:
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing
International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
Keywords:
lncRNA, cancer, lncRNA-cancer associations, machine learning, deep learning, case studies.
Abstract: In recent years, long non-coding RNAs (lncRNAs) have played important roles in various
biological processes. Mutations and regulation of lncRNAs are closely associated with many
human cancers. Predicting potential lncRNA-cancer associations helps to understand cancer's
pathogenesis and provides new ideas and approaches for cancer prevention, treatment and diagnosis.
Predicting lncRNA-cancer associations based on computational methods helps systematic biological
studies. In particular, machine learning methods have received much attention and are
commonly used to solve these problems. Therefore, many machine learning computational models
have been proposed to improve the prediction performance and achieve accurate diagnosis and
effective treatment of cancer. This review provides an overview of existing models for predicting
lncRNA-cancer associations by machine learning methods. The evaluation metrics of each model
are briefly described, analyzed the advantages and limitations of these models are analyzed. We
also provide a case study summary of the two cancers listed. Finally, the challenges and future
trends of predicting lncRNA-cancer associations with machine learning methods are discussed.
