Multi-class boosting for the analysis of multiple incomplete views on microbiome data.

BACKGROUND: Microbiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed to ML methods can be subject to different sampling, sequencing and preprocessing techniques. Each different choice in the pipeline can lead to a different view (i.e., feature set) of the same individuals, that classical (single-view) ML approaches may fail to simultaneously consider. Moreover, some views may be incomplete, i.e., some individuals may be missing in some views, possibly due to the absence of some measurements or to the fact that some features are not available/applicable for all the individuals. Multi-view learning methods can represent a possible solution to consider multiple feature sets for the same individuals, but most existing multi-view learning methods are limited to binary classification tasks or cannot work with incomplete views. RESULTS: We propose irBoost.SH, an extension of the multi-view boosting algorithm rBoost.SH, based on multi-armed bandits. irBoost.SH solves multi-class classification tasks and can analyze incomplete views. At each iteration, it identifies one winning view using adversarial multi-armed bandits and uses its predictions to update a shared instance weight distribution in a learning process based on boosting. In our experiments, performed on 5 multi-view microbiome datasets, the model learned by irBoost.SH always outperforms the best model learned from a single view, its closest competitor rBoost.SH, and the model learned by a multi-view approach based on feature concatenation, reaching an improvement of 11.8% of the F1-score in the prediction of the Autism Spectrum disorder and of 114% in the prediction of the Colorectal Cancer disease. CONCLUSIONS: The proposed method irBoost.SH exhibited outstanding performances in our experiments, also compared to competitor approaches. The obtained results confirm that irBoost.SH can fruitfully be adopted for the analysis of microbiome data, due to its capability to simultaneously exploit multiple feature sets obtained through different sequencing and preprocessing pipelines.

Assessing the dual-mycorrhizal status of a widespread tree species as a model for studies on stand biogeochemistry.

Viewing plant species by their mycorrhizal type has explained a range of ecosystem processes. However, mycorrhizal type is confounded with plant phylogeny and the environments in which mycorrhizal partners occur. To circumvent these confounding effects, "dual-mycorrhizal" plant species may be potential models for testing the influence of mycorrhizal type on stand biogeochemistry. To assess their use as models, duality in mycorrhizas within a single host species must be confirmed and factors underlying their variation understood. We surveyed roots, soils, and leaves of mature aspen (Populus tremuloides) across 27 stands in western Canada spanning two biomes: boreal forest and parklands. Aspen roots were mostly ectomycorrhizal with sporadic and rare occurrences of arbuscular mycorrhizas. We further tested whether a climate moisture index predicted abundance of ectomycorrhizal roots (number of ectomycorrhizal root tips m-1 root length) surveyed at two depths (0-20 cm and 20-40 cm) and found that ectomycorrhizal root abundance in subsoils (20-40 cm) was positively related to the index. We subsequently examined the relationships between ectomycorrhizal root abundance, leaf traits, and slow and fast pools of soil organic carbon and nitrogen. The ratio of leaf lignin:N, but not its components, increased along with ectomycorrhizal root abundance in subsoils. Soil carbon and nitrogen pools were independent of ectomycorrhizal root abundance. Our results suggest that (1) categorizing aspen as dual-mycorrhizal may overstate the functional importance of arbuscular mycorrhizas in this species and life stage, (2) water availability influences ectomycorrhizal root abundance, and (3) ectomycorrhizal root abundance coincides with leaf quality.

Food and lifestyle impact on breath VOCs using portable mass spectrometer-pilot study across European countries.

In the modern world, many people are changing old dietary and lifestyle habits to improve the quality of their living-to treat or just prevent possible diseases. The main goal of this pilot study was to assess the food and lifestyle impact on exhaled breath volatile organic compounds (VOCs) in various population groups. It was done by employing a recently validated portable membrane-inlet mass spectrometer-MIMS. Thus, the obtained results would also represent the additional confirmation for the employment of the new instrument in the breath analysis. The pilot study involved 151 participants across Europe, including people with overweight, obesity, type 2 diabetes mellitus, cardiovascular disease, people with poor-quality diet and professional athletes. Exhaled breath acetone, ethanol, isoprene, and n-pentane levels were determined in samples before the meal, and 120 min after the meal. Obtained basal ppbvvalues were mainly in accordance with previously reported, which confirms that MIMS instrument can be used in the breath analysis. Combining the quantified levels along with the information about the participants' lifestyle habits collected via questionnaire, an assessment of the food and lifestyle impact was obtained. Notable alteration in examined VOC levels upon meal consumption was detected in more than 70% of all participants, with exception for isoprene, which was affected in about half of participants. Lifestyle parameters impact was examined using statistical analysis of variance (ANOVA) on ranks test. Statistically significant differences in basal breath VOC levels were observed among all examined population groups. Also, n-pentane and ethanol levels significantly differed in people of different ages, as well as acetone levels in people with different physical activity habits. These findings are promising for further, more focused research using MIMS technique in breath analysis.

Overview of data preprocessing for machine learning applications in human microbiome research.

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Machine learning approaches in microbiome research: challenges and best practices.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.