基于质谱的蛋白质组学中应对数据挑战的机器学习策略。

IF 3.1 2区 化学 Q2 BIOCHEMICAL RESEARCH METHODS
Ceder Dens, Charlotte Adams, Kris Laukens, Wout Bittremieux
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引用次数: 0

摘要

在计算蛋白质组学中,机器学习(ML)已成为加强数据分析的重要工具。尽管取得了重大进展,但 ML 模型架构的多样性和蛋白质组学数据的复杂性给这些工具的有效开发和评估带来了巨大挑战。在此,我们强调了训练 ML 模型所需的高质量、全面的数据集的必要性,并倡导数据标准化以支持稳健的模型开发。我们强调了 ProteomeTools 和 MassIVE-KB 等关键数据集在推动蛋白质组学中的 ML 应用方面的重要作用,并讨论了数据集大小对模型性能的影响,强调较大的数据集通常能产生更准确的模型。为了解决数据稀缺的问题,我们探讨了自监督预训练和多任务学习等算法策略。最后,我们希望这次讨论能呼吁蛋白质组学界行动起来,在数据标准化和收集工作方面开展合作,这对该领域 ML 方法的可持续发展和完善至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics.

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics.

In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.

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来源期刊
CiteScore
5.50
自引率
9.40%
发文量
257
审稿时长
1 months
期刊介绍: The Journal of the American Society for Mass Spectrometry presents research papers covering all aspects of mass spectrometry, incorporating coverage of fields of scientific inquiry in which mass spectrometry can play a role. Comprehensive in scope, the journal publishes papers on both fundamentals and applications of mass spectrometry. Fundamental subjects include instrumentation principles, design, and demonstration, structures and chemical properties of gas-phase ions, studies of thermodynamic properties, ion spectroscopy, chemical kinetics, mechanisms of ionization, theories of ion fragmentation, cluster ions, and potential energy surfaces. In addition to full papers, the journal offers Communications, Application Notes, and Accounts and Perspectives
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