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

Utveckling av en produktionslayout i en digital mikrofabrik : En studie om montering av husmoduler

This bachelor thesis is carried out in collaboration with Husmuttern AB. Husmuttern AB is a company that develops modular houses to be a manufacturer. The company also developed assembly stations to manufacture these modules for the house, which still is a concept in form of 3D-drawings and models. The goal of this thesis is to develop a factory layout for manufacturing of wall 250 containing these assembly stations and material needed. The purpose of this study is to bring a factory drawing for Husmuttern AB for the assembly of house modules, where the study focuses on material supply, placement, and production layout. The factory drawing is adapted to possibly be sold as a product so a limited area for assembly is desired. Three research questions have been formulated as guidelines: • How should assembly fixtures and materials be positioned for efficient workflow? • Can we reduce waste in micro factories using a digital factory? • How can Augmented reality aid the process in terms of visual understanding? The study begins with a literature review followed by empirical data collected at Husmuttern AB through interviews. In the section on Empirical results, the factory components, sketches, concepts, relevant data and how the project was validated by employees are presented. To gain an understanding of the space required, lean philosophies, recommendations for work environments, and production processes were studied. To structure the work and have continually progress in the study, the foundation of the product development process was used, as the project team viewed the factory as a product. The result is a proposal for the layout with assembly stations and materials positioned. The result was validated with the help of the staff, who were able to walk around in the factory using their mobile phones and AR

Utveckling av en produktionslayout i en digital mikrofabrik : En studie om montering av husmoduler

This bachelor thesis is carried out in collaboration with Husmuttern AB. Husmuttern AB is a company that develops modular houses to be a manufacturer. The company also developed assembly stations to manufacture these modules for the house, which still is a concept in form of 3D-drawings and models. The goal of this thesis is to develop a factory layout for manufacturing of wall 250 containing these assembly stations and material needed. The purpose of this study is to bring a factory drawing for Husmuttern AB for the assembly of house modules, where the study focuses on material supply, placement, and production layout. The factory drawing is adapted to possibly be sold as a product so a limited area for assembly is desired. Three research questions have been formulated as guidelines: • How should assembly fixtures and materials be positioned for efficient workflow? • Can we reduce waste in micro factories using a digital factory? • How can Augmented reality aid the process in terms of visual understanding? The study begins with a literature review followed by empirical data collected at Husmuttern AB through interviews. In the section on Empirical results, the factory components, sketches, concepts, relevant data and how the project was validated by employees are presented. To gain an understanding of the space required, lean philosophies, recommendations for work environments, and production processes were studied. To structure the work and have continually progress in the study, the foundation of the product development process was used, as the project team viewed the factory as a product. The result is a proposal for the layout with assembly stations and materials positioned. The result was validated with the help of the staff, who were able to walk around in the factory using their mobile phones and AR

Microbiome-based risk prediction in incident heart failure : a community challenge

Preprin

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

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

Peer reviewed: TrueAcknowledgements: The authors are grateful to all COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies” members for their contribution to the COST Action objectives, and to COST (European Cooperation in Science and Technology) for the economic support, www.cost.eu. WG2 and WG3 thank Emmanuelle Le Chatelier and Pauline Barbet (Université Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France) for preparing the shotgun CRC benchmark dataset.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

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

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

A toolbox of machine learning software to support microbiome analysis

Peer reviewed: TrueAcknowledgements: This article is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies,” CA18131, supported by COST (European Cooperation in Science and Technology), www.cost.eu.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.</jats:p