School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
Biochem Biophys Res Commun. 2024 Sep 10;724:150225. doi: 10.1016/j.bbrc.2024.150225. Epub 2024 Jun 4.
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.
转录组学研究的数据采集曾经是转录组分析管道中的瓶颈。然而，最近转录组分析技术的发展提高了研究人员获取数据的能力，研究重点因此转移到数据分析上。将机器学习纳入传统分析方法使得处理更大、更复杂的数据的可能性更高。许多生物信息学家，尤其是那些不熟悉人类转录组学和复杂生物系统中的机器学习的人，面临着一个巨大的障碍，这源于他们对该领域中机器学习应用的当前现状的认识有限。为了解决这一差距，本综述旨在向这些人介绍一般类型的机器学习，接着介绍更具体的一系列技术，并通过将这些技术纳入人类转录组研究的分析管道的例子进行演示。涵盖了重要的计算方面，如数据预处理、任务制定、结果（机器学习模型的性能）和验证方法。为了更好地实现实际相关性，本文强烈关注在过去五年内发表的研究，几乎完全检查人类转录组，并将结果与标准的非机器学习工具进行比较。
