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Advances in Data Repositories for ncRNA-Protein Interaction Predictions Based on Machine Learning: A Mini-Review

机译：基于机器学习的ncRNA-蛋白质相互作用预测数据存储库进展：小型综述

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摘要

Background: This study aims at exploring the advances in data repositories for predicting interactions between non-coding RNAs (ncRNAs) and corresponding proteins. NcRNAs are a class of ribonucleic acid that lacks the potential for protein translation. A series of studies indicated that ncRNAs play critical roles in epigenetic regulations, chromatin remodeling, transcription process, and post-transcriptional processing. Since ncRNAs function with associated proteins during complex biological procedures, it is important to identify ncRNA-protein interactions, which will provide guidance for exploring the internal molecular mechanisms. Recently, a variety of machine learning methods have emerged, with the lower cost and time-saving advantages compared to experimental methods. In machine learning, the performance of classification models is often affected by the quality of input samples and their features. Aims: Thus, the study intends to introduce the related data sources used in predicting ncRNA-protein interactions (ncRPIs) based on machine learning. Methods: We searched related literature from different sources, including PubMed, Web of Science, and Scopus, using the search terms “machine learning”, “repository”, “non-coding RNA”, and “protein”. In this work, we described the databases applied to the dataset construction and feature representation in the ncRPIs prediction task. Results: This study reviews the application of the benchmark dataset construction and conventional feature representation during ncRPI prediction processes. Furthermore, the source, main functions, and development status of each database are also discussed in this work. Conclusion: With the development of high-throughput technologies for generating ncRPIs and constructing related databases, machine learning would become a necessary research means, enriching the prediction methods of ncRPIs. Due to an increase in improved databases, the resources of molecular structures, functions, and genetic information for data mining have increased, enhancing the credibility of ncRPI prediction based on machine learning. We believe that the databases will be more widely used in disease research, drug development, and many other fields.
机译：背景:本研究的目的是探索数据存储库的发展预测这些非蛋白非编码rna之间的相互作用相应的蛋白质。核糖核酸,缺乏的潜力蛋白质的翻译。表明ncRNAs中扮演关键的角色表观遗传规则,染色质重塑,转录过程,转录后处理。在复杂的生物过程,它的蛋白质重要的是要确定ncRNA-protein吗相互作用,这将提供指导探索内在的分子机制。最近,各种各样的机器学习方法出现,降低成本和节省时间的实验方法相比的优势。机器学习的性能分类模型往往是影响输入样本的质量和他们的特性。目的:研究因此,打算介绍相关数据来源用于预测(ncRPIs)基于ncRNA-protein交互机器学习。文献从不同的来源,包括PubMed、网络科学、和斯高帕斯,使用搜索词“机器学习”,“库”,“非编码RNA”,“蛋白质”。描述了数据库应用于数据集的建设和功能表示ncRPIs预测任务。审查基准数据集上的应用建设和传统功能ncRPI预测过程中表示。此外,源代码,主要功能,每个数据库的发展现状讨论了这项工作。高通量技术的发展生成ncRPIs和建设相关的数据库、机器学习将成为一个必要的研究手段,丰富ncRPIs的预测方法。在改进的数据库中,分子的资源结构、功能和遗传信息对数据挖掘有增加,提高ncRPI预测基于机器的可信度学习。更广泛地用于疾病研究、药物发展,和许多其他领域。

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