Moreno-Indias Isabel, Lahti Leo, Nedyalkova Miroslava, Elbere Ilze, Roshchupkin Gennady, Adilovic Muhamed, Aydemir Onder, Bakir-Gungor Burcu, Santa Pau Enrique Carrillo-de, D'Elia Domenica, Desai Mahesh S, Falquet Laurent, Gundogdu Aycan, Hron Karel, Klammsteiner Thomas, Lopes Marta B, Marcos-Zambrano Laura Judith, Marques Cláudia, Mason Michael, May Patrick, Pašić Lejla, Pio Gianvito, Pongor Sándor, Promponas Vasilis J, Przymus Piotr, Saez-Rodriguez Julio, Sampri Alexia, Shigdel Rajesh, Stres Blaz, Suharoschi Ramona, Truu Jaak, Truică Ciprian-Octavian, Vilne Baiba, Vlachakis Dimitrios, Yilmaz Ercument, Zeller Georg, Zomer Aldert L, Gómez-Cabrero David, Claesson Marcus J
Instituto de Investigación Biomédica de Málaga (IBIMA), Unidad de Gestión Clìnica de Endocrinologìa y Nutrición, Hospital Clìnico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain.
Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
Front Microbiol. 2021 Feb 22;12:635781. doi: 10.3389/fmicb.2021.635781. eCollection 2021.
The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
人类微生物组已成为人类生物学和生物医学的核心研究主题。当前的微生物组研究在不同身体部位、人群和生命阶段生成高通量组学数据。微生物组研究中的许多挑战与其他高通量研究类似，定量分析需要解决数据的异质性、特定的统计特性，以及个体和身体部位之间微生物组组成的显著差异。这导致了一系列广泛的统计和机器学习挑战，涵盖从研究设计、数据处理、标准化到分析、建模、跨研究比较、预测、数据科学生态系统以及可重复报告等方面。然而，尽管已经开发了许多统计和机器学习方法及工具，但仍需要新技术来应对新兴应用和微生物组数据的巨大异质性。我们回顾并讨论统计和机器学习技术在人类微生物组研究中的新兴应用，并介绍了COST行动CA18131“ML4Microbiome”，该行动将微生物组研究人员和机器学习专家聚集在一起，以应对当前的挑战，如分析管道的标准化以实现数据分析结果的可重复性、对现有和新工具及本体进行基准测试、改进或开发。
