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.

FooDrugs: a comprehensive food-drug interactions database with text documents and transcriptional data.

Food-drug interactions (FDIs) occur when a food item alters the pharmacokinetics or pharmacodynamics of a drug. FDIs can be clinically relevant, as they can hamper or enhance the therapeutic effects of a drug and impact both their efficacy and their safety. However, knowledge of FDIs in clinical practice is limited. This is partially due to the lack of resources focused on FDIs. Here, we describe FooDrugs, a database that centralizes FDI knowledge retrieved from two different approaches: a natural processing language pipeline that extracts potential FDIs from scientific documents and clinical trials and a molecular similarity approach based on the comparison of gene expression alterations caused by foods and drugs. FooDrugs database stores a total of 3 430 062 potential FDIs, with 1 108 429 retrieved from scientific documents and 2 321 633 inferred from molecular data. This resource aims to provide researchers and clinicians with a centralized repository for potential FDI information that is free and easy to use. Database URL:  https://zenodo.org/records/8192515 Database DOI:  https://doi.org/10.5281/zenodo.6638469.

Citizen science helps to raise awareness about gut microbiome health in people at risk of developing non-communicable diseases.

Citizens lack knowledge about the impact of gut microbiota on health and how lifestyle and dietary choices can influence it, leading to Non-Communicable Diseases (NCDs) and affecting overall well-being. Participatory action research (PAR) is a promising approach to enhance communication and encourage individuals to adopt healthier behaviors and improve their health. In this study, we explored the feasibility of integrating the photovoice method with citizen science approaches to assess the impact of social and environmental factors on gut microbiota health. In this context, citizen science approaches entailed the involvement of participants in the collection of samples for subsequent analysis, specifically gut microbiome assessment via 16S rRNA gene sequencing. We recruited 70 volunteers and organized six photovoice groups based on age and educational background. Participants selected 64 photographs that represented the influence of daily habits on gut microbiota health and created four photovoice themes. Analysis of the gut microbiome using 16S rRNA gene sequencing identified 474 taxa, and in-depth microbial analysis revealed three clusters of people based on gut microbiome diversity and body mass index (BMI). Our findings indicate that participants enhanced their knowledge of gut microbiome health through PAR activities, and we found a correlation between lower microbial diversity, higher BMI, and better achievement of learning outcomes. Using PAR as a methodology is an effective way to increase citizens' awareness and engagement in self-care, maintain healthy gut microbiota, and prevent NCD development. These interventions are particularly beneficial for individuals at higher risk of developing NCDs.

AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence.

The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research. Database URL https://github.com/AI4Food/AI4FoodDB.