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  • 1
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 12 ( 2021-2-19)
    Abstract: The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
    Type of Medium: Online Resource
    ISSN: 1664-302X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2587354-4
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  • 2
    Online Resource
    Online Resource
    Frontiers Media SA ; 2019
    In:  Frontiers in Microbiology Vol. 10 ( 2019-8-6)
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 10 ( 2019-8-6)
    Type of Medium: Online Resource
    ISSN: 1664-302X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2019
    detail.hit.zdb_id: 2587354-4
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  • 3
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 13 ( 2022-4-11)
    Abstract: Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.
    Type of Medium: Online Resource
    ISSN: 1664-302X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2587354-4
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  • 4
    Online Resource
    Online Resource
    Frontiers Media SA ; 2018
    In:  Frontiers in Cardiovascular Medicine Vol. 5 ( 2018-7-17)
    In: Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 5 ( 2018-7-17)
    Type of Medium: Online Resource
    ISSN: 2297-055X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2018
    detail.hit.zdb_id: 2781496-8
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  • 5
    In: Frontiers in Medicine, Frontiers Media SA, Vol. 8 ( 2021-4-6)
    Abstract: Remaining a major healthcare concern with nearly 29 million confirmed cases worldwide at the time of writing, novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused more than 920 thousand deaths since its outbreak in China, December 2019. First case of a person testing positive for SARS-CoV-2 infection within the territory of the Republic of Latvia was registered on 2nd of March 2020, 9 days prior to the pandemic declaration by WHO. Since then, more than 277,000 tests were carried out confirming a total of 1,464 cases of coronavirus disease 2019 (COVID-19) in the country as of 12th of September 2020. Rapidly reacting to the spread of the infection, an ongoing sequencing campaign was started mid-March in collaboration with the local testing laboratories, with an ultimate goal in sequencing as much local viral isolates as possible, resulting in first full-length SARS-CoV-2 isolate genome sequences from the Baltics region being made publicly available in early April. With 133 viral isolates representing ~9.1% of the total COVID-19 cases during the “first coronavirus wave” in the country (early March, 2020—mid-September, 2020) being completely sequenced as of today, here, we provide a first report on the genetic diversity of Latvian SARS-CoV-2 isolates.
    Type of Medium: Online Resource
    ISSN: 2296-858X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2775999-4
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  • 6
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 12 ( 2021-2-22)
    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.
    Type of Medium: Online Resource
    ISSN: 1664-302X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2587354-4
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  • 7
    Online Resource
    Online Resource
    Frontiers Media SA ; 2021
    In:  Frontiers in Water Vol. 3 ( 2021-12-15)
    In: Frontiers in Water, Frontiers Media SA, Vol. 3 ( 2021-12-15)
    Abstract: Background: Biofilms, when formed on the surfaces of water pipes, can be responsible for a wide range of water quality and operational problems. We sought to assess the bacterial and free-living protozoa (FLP) diversity, in relation to the presence of Legionnaire's disease-causing bacteria Legionella pneumophila ( L. pneumophila ) in 45 biofilms of hot water distribution system pipes of apartment buildings in Riga, the capital city of Latvia. Results: 16S rRNA amplicon sequencing (metataxonomics) revealed that each biofilm contained 224 rather evenly distributed bacterial genera and that most common and most abundant were two genera, completely opposites in terms of their oxygen requirements: the obligately anaerobic Thermodesulfovibrio and the strictly aerobic Phenylobacterium . Water temperature and north-south axis (i.e., different primary water sources) displayed the most significant effect on the inter-sample variations, allowing us to re-construct three sub-networks (modules) of co-occurring genera, one involving (potentially FLP-derived) Legionella spp. Pangenome-based functional profile predictions suggested that all three may be dominated by pathways related to the development and maintenance of biofilms, including quorum sensing and nutrient transport, as well as the utilization of various energy sources, such as carbon and nitrogen. In our 18S rRNA amplicon sequencing data, potential hosts of L. pneumophila were detected in 11 out of 12 biofilm samples analyzed, however, in many cases, their relative abundance was very low ( & lt;1%). By validating our findings using culture-based methods, we detected L. pneumophila (serogroups 2, 3, 6 and 9) in nine (20%) biofilms, whereas FLP (mostly Acanthamoeba, Vahlkampfidae and Vermamoeba spp.) were present in six (~13%) biofilms. In two biofilms, L. pneumophila and its potential hosts were detected simultaneously, using culture-based methods. Conclusions: Overall, our study sheds light on the community diversity of hot water biofilms and predicts how several environmental factors, such as water temperature and source might shape it.
    Type of Medium: Online Resource
    ISSN: 2624-9375
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2986721-6
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