Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body’s normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

中文翻译：

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基于深度学习的长链非编码RNA识别与预测相互作用机制综合综述


随着测序技术和基因组学研究的进步，长非编码RNA（lncRNA）被发现广泛参与真核生物的表观遗传、转录和转录后调控过程。因此，它们在人体的正常生理和各种疾病结果中发挥着至关重要的作用。目前，大量未知的lncRNA测序数据需要探索。建立基于深度学习的 lncRNA 预测模型为研究人员提供了宝贵的见解，大大减少了与试错相关的时间和成本，并随着人工智能时代的进步，促进疾病相关 lncRNA 识别，用于预后分析和靶向药物开发。然而，大多数lncRNA相关研究人员对深度学习模型的最新进展以及lncRNA功能研究中的模型选择和应用缺乏认识。因此，我们阐明深度学习模型的概念，探讨几种流行的深度学习算法及其数据偏好，结合各种预测函数对过去 5 年来具有示范性预测性能的最新文献研究进行全面回顾，批判性地分析和讨论当前深度学习模型和解决方案的优点和局限性，同时还根据 lncRNA 研究的前沿进展提出了前景。