Abstract：
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.
摘要：
已发现长链非编码RNA(lncRNAs)广泛参与真核表观遗传,转录，和转录后调控过程随着测序技术和基因组学研究的进步。因此,它们在人体的正常生理和各种疾病的结局中起着至关重要的作用。目前,许多未知的lncRNA测序数据需要探索。建立基于深度学习的lncRNAs预测模型为研究人员提供了有价值的见解，随着人工智能时代的发展，大幅减少与试验和错误相关的时间和成本，并促进疾病相关的lncRNA鉴定，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，我们阐明了深度学习模型的概念，探索几种流行的深度学习算法及其数据偏好，结合不同的预测函数，对过去5年中具有示例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，同时也提出了基于lncRNA研究前沿进展的前景。
