ImmunoAdept – bringing blood microbiome profiling to the clinical practice

Walsh, Paul; Andrade, Bruno de Bezerril; Palu, Cintia; Wu, Jason; Lawlor, Brendan; Kelly, Brian; Hemmje, Matthias; Kramer, Michael

doi:10.1109/bibm.2018.8621354

Cited by 2 publications

(1 citation statement)

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“…Starting from training data where both the metagenomic sequences and the associated phenotype are known, the classifier learns to apply the categories to previously unseen data. This helps us to answer questions like "How can sick and healthy individuals be distinguished by their blood microbiome" [65], "How does the maternal microbiome affect the development of newborns?" [1], "What role do microbiomes play in the relationship between diet and metabolism?"…”

Section: Phenotype Classificationmentioning

confidence: 99%

Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

et al. 2021

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Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.

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Section: Phenotype Classificationmentioning

confidence: 99%