Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
Drug Discov Today. 2023 Feb;28(2):103432. doi: 10.1016/j.drudis.2022.103432. Epub 2022 Nov 10.
Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.
基因突变和长非编码 RNA（lncRNA）的失调与各种人类复杂疾病的发生密切相关，但仅有少数 lncRNA 被实验证实与人类疾病相关。预测新的潜在 lncRNA-疾病关联（LDA）将有助于我们理解人类疾病的发病机制，以及检测疾病标志物，从而应用于疾病的诊断、预防和治疗。计算方法可以有效地缩小生物实验的筛选范围，从而缩短此类实验的周期和降低成本。在这篇综述中，我们概述了预测 LDA 的计算方法的最新进展，重点介绍了 LDA 数据库、lncRNA/疾病相似性计算以及先进的计算模型。此外，我们还分析了各种计算模型的局限性，并讨论了未来的挑战和发展方向。
