摘要
背景:采用生物标志物作为高通量、复杂微阵列或测序数据的一部分,需要通过机器学习来发现和验证这些数据。由于机器学习在相关生物标志物的特征提取以及样本分类作为已发现生物标志物的验证方面的有效性和效率,它仍然是一种基本且不可或缺的工具。 目标:本综述旨在展示各种机器学习方法和模型在处理质谱、微阵列和 DNA/RNA 序列数据中发现的高通量、高维数据方面的影响和能力;在使用机器学习之前排除生物标志物发现的数据。 方法:对大量强调机器学习用于生物标志物发现的文献进行了审查,最终确定了 21 种机器学习算法/网络和 3 种组合架构,涵盖 17 个研究领域。对这些文献进行了筛选,以研究在生物标志物发现框架内机器学习的使用和发展。 结果:在收集的 93 篇论文中,对不同子领域的总共 62 项生物标志物研究进行了进一步审查——其中 49 项采用了机器学习算法,其中 13 项采用了基于神经网络的模型。通过在生物标志物相关机器学习方法中应用、创新和创建工具,它的使用允许在不同数据格式、来源和研究领域中发现、积累、验证和解释生物标志物。 结论:使用机器学习方法进行生物标志物发现对于分析用于生物标志物发现的各种类型的数据至关重要,例如质谱、核苷酸和蛋白质测序以及图像(例如 CT 扫描)数据。进一步研究更标准化的评估技术,以及使用尖端机器学习架构可能会导致更准确和具体的结果。
关键词: 生物标志物发现、机器学习、神经网络、质谱、测序、微阵列。
Current Medicinal Chemistry
Title:Advancements within Modern Machine Learning Methodology: Impacts and Prospects in Biomarker Discovery
Volume: 28 Issue: 32
关键词: 生物标志物发现、机器学习、神经网络、质谱、测序、微阵列。
摘要:
Background: The adoption of biomarkers as part of high-throughput, complex microarray or sequencing data has necessitated the discovery and validation of these data through machine learning. Machine learning has remained a fundamental and indispensable tool due to its efficacy and efficiency in both feature extraction of relevant biomarkers as well as the classification of samples as validation of the discovered biomarkers.
Objectives: This review aims to present the impact and ability of various machine learning methodologies and models to process high-throughput, high-dimensionality data found within mass spectrometry, microarray, and DNA/RNA-sequence data; data that precluded biomarker discovery prior to the use of machine learning.
Methods: A vast array of literature highlighting machine learning for biomarker discovery was reviewed, resulting in the eligibility of 21 machine learning algorithms/networks and 3 combinatory architectures, spanning 17 fields of study. This literature was screened to investigate the usage and development of machine learning within the framework of biomarker discovery.
Results: Out of the 93 papers collected, a total of 62 biomarker studies were further reviewed across different subfields-49 of which employed machine learning algorithms, and 13 of which employed neural network-based models. Through the application, innovation, and creation of tools in biomarker-related machine learning methodologies, its use allowed for the discovery, accumulation, validation, and interpretation of biomarkers within varied data formats, sources, as well as fields of study.
Conclusion: The use of machine learning methodologies for biomarker discovery is critical to the analysis of various types of data used for biomarker discovery, such as mass spectrometry, nucleotide and protein sequencing, and image (e.g. CT-scan) data. Further studies containing more standardized techniques for evaluation, and the use of cutting- edge machine learning architectures may lead to more accurate and specific results.
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Cite this article as:
Advancements within Modern Machine Learning Methodology: Impacts and Prospects in Biomarker Discovery, Current Medicinal Chemistry 2021; 28 (32) . https://dx.doi.org/10.2174/0929867328666210208111821
DOI https://dx.doi.org/10.2174/0929867328666210208111821 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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