MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Jens Settelmeier, Sandra Goetze, Julia Boshart, Jianbo Fu, Amanda Khoo, Sebastian N Steiner, Martin Gesell, Jacqueline Hammer, Peter J Schüffler, Diyora Salimova, Patrick G A Pedrioli, Bernd Wollscheid
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引用次数: 0

Abstract

MultiOmicsAgent (MOAgent) is an innovative, Python-based open-source tool for biomarker discovery, utilizing machine learning techniques, specifically extreme gradient-boosted decision trees, to process multiomics data. With its cross-platform compatibility, user-oriented graphical interface, and well-documented API, MOAgent not only meets the needs of both coding professionals and those new to machine learning but also addresses common data analysis challenges like normalization, data incompleteness, class imbalances and data leakage between disjoint data splits. MOAgent″s guided data analysis strategy opens up data-driven insights from digitized clinical biospecimen cohorts, making advanced data analysis accessible and reliable for a wide audience.

MultiOmicsAgent:基于引导的极端梯度增强决策树的方法,用于在多组学数据中发现候选生物标志物。
MultiOmicsAgent (MOAgent)是一个创新的、基于python的生物标志物发现开源工具,利用机器学习技术,特别是极端梯度增强决策树,来处理多组学数据。MOAgent具有跨平台兼容性、面向用户的图形界面和文档完备的API,不仅可以满足编码专业人员和机器学习新手的需求,还可以解决常见的数据分析挑战,如规范化、数据不完整、类不平衡和不相交数据分割之间的数据泄漏。MOAgent″的指导数据分析策略从数字化临床生物标本队列中开辟了数据驱动的见解,为广大受众提供了可访问和可靠的高级数据分析。
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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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