LncRNA-protein interaction prediction with reweighted feature selection

• Published in: BMC bioinformatics 2023-10, Vol.24 (1), p.410-16, Article 410
• Main Authors: Lv, Guohao, Xia, Yingchun, Qi, Zhao, Zhao, Zihao, Tang, Lianggui, Chen, Cheng, Yang, Shuai, Wang, Qingyong, Gu, Lichuan
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Amino acids; Biological activity; Boosting; Computational Biology - methods; Deep learning; Feature selection; LncRNA–protein prediction; Machine learning; Methods; Non-coding RNA; Observational learning; Predictions; Protein interaction; Protein sequence; Proteins; Redundancy; Reweighting; RNA, Long Noncoding - genetics
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05536-1

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.