Guo Chang, Wang Xiaoli, Ren Han
Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510420, China.
Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Comput Struct Biotechnol J. 2024 Jan 22;23:813-833. doi: 10.1016/j.csbj.2024.01.011. eCollection 2024 Dec.
Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that plays important roles in many biological processes and major cancer diagnosis and treatment, thus becoming a hot research topic. This study aims to provide an in-depth review of computational piRNA-related research, including databases and computational models. Herein, we perform literature analysis and use comparative evaluation methods to summarize and analyze three aspects of computational piRNA-related research: (i) computational models for piRNA-related molecular identification tasks, (ii) computational models for piRNA-disease association prediction tasks, and (iii) computational resources and evaluation metrics for these tasks. This study shows that computational piRNA-related research has significantly progressed, exhibiting promising performance in recent years, whereas they also suffer from the emerging challenges of inconsistent naming systems and the lack of data. Different from other reviews on piRNA-related identification tasks that focus on the organization of datasets and computational methods, we pay more attention to the analysis of computational models, algorithms, and performances that aim to provide valuable references for computational piRNA-related identification tasks. This study will benefit the theoretical development and practical application of piRNAs by better understanding computational models and resources to investigate the biological functions and clinical implications of piRNA.
Piwi相互作用RNA（piRNA）是一类小的非编码RNA（ncRNA），在许多生物学过程以及癌症的主要诊断和治疗中发挥着重要作用，因此成为一个热门的研究课题。本研究旨在对与piRNA相关的计算研究进行深入综述，包括数据库和计算模型。在此，我们进行文献分析并使用比较评估方法，对与piRNA相关的计算研究的三个方面进行总结和分析：（i）用于piRNA相关分子识别任务的计算模型，（ii）用于piRNA-疾病关联预测任务的计算模型，以及（iii）这些任务的计算资源和评估指标。本研究表明，与piRNA相关的计算研究取得了显著进展，近年来表现出良好的性能，然而它们也面临着命名系统不一致和数据缺乏等新出现的挑战。与其他专注于数据集组织和计算方法的piRNA相关识别任务综述不同，我们更关注计算模型、算法和性能的分析，旨在为与piRNA相关的识别任务提供有价值的参考。通过更好地理解计算模型和资源来研究piRNA的生物学功能和临床意义，本研究将有助于piRNA的理论发展和实际应用。
