Recent Advances in Machine Learning Methods for LncRNA-Cancer Associations Prediction

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.

Cite this article as:

Wang Ruobing, Meng Lingyu and Tan Jianjun*, Recent Advances in Machine Learning Methods for LncRNA-Cancer Associations Prediction, Current Chinese Science 2024; 4 (3) . https://dx.doi.org/10.2174/0122102981299289240324072639