Dataset Information

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

ABSTRACT: 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.

SUBMITTER: Moreno-Indias I

PROVIDER: S-EPMC7937616 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

Moreno-Indias Isabel I Lahti Leo L Nedyalkova Miroslava M Elbere Ilze I Roshchupkin Gennady G Adilovic Muhamed M Aydemir Onder O Bakir-Gungor Burcu B Santa Pau Enrique Carrillo-de EC D'Elia Domenica D Desai Mahesh S MS Falquet Laurent L Gundogdu Aycan A Hron Karel K Klammsteiner Thomas T Lopes Marta B MB Marcos-Zambrano Laura Judith LJ Marques Cláudia C Mason Michael M May Patrick P Pašić Lejla L Pio Gianvito G Pongor Sándor S Promponas Vasilis J VJ Przymus Piotr P Saez-Rodriguez Julio J Sampri Alexia A Shigdel Rajesh R Stres Blaz B Suharoschi Ramona R Truu Jaak J Truică Ciprian-Octavian CO Vilne Baiba B Vlachakis Dimitrios D Yilmaz Ercument E Zeller Georg G Zomer Aldert L AL Gómez-Cabrero David D Claesson Marcus J MJ

Frontiers in microbiology 20210222

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. ...[more]

PMID: 33692771

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Project description:Objective: This study aimed to systematically assess the characteristics and risk of bias of previous studies that have investigated nonlinear machine learning algorithms for warfarin dose prediction. Methods: We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), and Wanfang Database up to March 2022. We assessed the general characteristics of the included studies with respect to the participants, predictors, model development, and model evaluation. The methodological quality of the studies was determined, and the risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: From a total of 8996 studies, 23 were assessed in this study, of which 23 (100%) were retrospective, and 11 studies focused on the Asian population. The most common demographic and clinical predictors were age (21/23, 91%), weight (17/23, 74%), height (12/23, 52%), and amiodarone combination (11/23, 48%), while CYP2C9 (14/23, 61%), VKORC1 (14/23, 61%), and CYP4F2 (5/23, 22%) were the most common genetic predictors. Of the included studies, the MAE ranged from 1.47 to 10.86 mg/week in model development studies, from 2.42 to 5.18 mg/week in model development with external validation (same data) studies, from 12.07 to 17.59 mg/week in model development with external validation (another data) studies, and from 4.40 to 4.84 mg/week in model external validation studies. All studies were evaluated as having a high risk of bias. Factors contributing to the risk of bias include inappropriate exclusion of participants (10/23, 43%), small sample size (15/23, 65%), poor handling of missing data (20/23, 87%), and incorrect method of selecting predictors (8/23, 35%). Conclusions: Most studies on nonlinear-machine-learning-based warfarin prediction models show poor methodological quality and have a high risk of bias. The analysis domain is the major contributor to the overall high risk of bias. External validity and model reproducibility are lacking in most studies. Future studies should focus on external validity, diminish risk of bias, and enhance real-world clinical relevance.

Dataset Information

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

Publications

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

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