Recent Advances in Predicting Protein-lncRNA Interactions Using Machine Learning Methods

Abstract

: Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.

BioPrediction: Democratizing Machine Learning in the Study of Molecular Interactions

“…Vem em pauta, por exemplo, estudos relacionados à interac ¸ão droga-droga [Li et al 2020], genegene [Cole et al 2017] e, finalmente, lncRNA-proteína [Ferrè et al 2016]. Considerando o alto custo de tempo e recursos necessários em análises experimentais para esse contexto, impulsionou-se a aplicac ¸ão de algoritmos de AM de alto desempenho para realizac ¸ão de predic ¸ão de interac ¸ões, com base em interac ¸ões já descobertas [Yu et al 2022].…”

Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Roots of Rice Seedlings under Nitrogen Deficiency

“…lncRNAs are known to play important roles in the plant response to stress, such as high salinity or pathogen infection [15,16]. Whole genome RNA sequencing (RNA-Seq) has been instrumental in the genome-wide identification of lncRNAs, including leading to the identification of 4373 lncRNAs in Arabidopsis thaliana, approximately 12,000 lncRNAs in Brassica napus, 10,761 lncRNAs in Zea mays (CANTATAdb2.0, http://cantata.amu.edu.pl/, 27 November 2023), and 2788 lncRNAs in O. sativa [17][18][19][20]. To date, several lncRNAs have also been functionally characterized.…”

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

“…Machine learning methods have been applied to various association discovery tasks (Zou et al, 2018;Peng et al, 2019;Shen et al, 2022;Wu et al, 2022;Yu et al, 2022;Lin et al, 2023a;Peng et al, 2023a;Peng et al, 2023b;Peng et al, 2024b;Han et al, 2023;Liu and Zhang, 2023;Qi and Zou, 2023;Xiong et al, 2023;Xu et al, 2024;Zhang et al, 2024). Consequently, machine learning algorithms have been broadly applied in LDA prediction, for example, collaborative filtering (Yu et al, 2019), graph regularization (Liu et al, 2020;, matrix factorization (Fu et al, 2018;Wang et al, 2020;Xi et al, 2022), heterogeneous graph learning framework, (Cao et al, 2023), and ensemble learning models (Peng et al, 2022a).…”