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Leonor Fernandes Saraiva JP, Zubiria-Barrera C, Klassert TE, et al. Combination of Classifiers Identifies Fungal-Specific Activation of Lysosome Genes in Human Monocytes. Front Microbiol. 2017;8:2366doi: 10.3389/fmicb.2017.02366.
Leonor Fernandes Saraiva, J. P., Zubiria-Barrera, C., Klassert, T. E., Lautenbach, M. J., Blaess, M., Claus, R. A., Slevogt, H., & König, R. (2017). Combination of Classifiers Identifies Fungal-Specific Activation of Lysosome Genes in Human Monocytes. Frontiers in microbiology, 82366. https://doi.org/10.3389/fmicb.2017.02366
Leonor Fernandes Saraiva, João P, et al. "Combination of Classifiers Identifies Fungal-Specific Activation of Lysosome Genes in Human Monocytes." Frontiers in microbiology vol. 8 (2017): 2366. doi: https://doi.org/10.3389/fmicb.2017.02366
Leonor Fernandes Saraiva JP, Zubiria-Barrera C, Klassert TE, Lautenbach MJ, Blaess M, Claus RA, Slevogt H, König R. Combination of Classifiers Identifies Fungal-Specific Activation of Lysosome Genes in Human Monocytes. Front Microbiol. 2017 Nov 29;8:2366. doi: 10.3389/fmicb.2017.02366. eCollection 2017. PMID: 29238336; PMCID: PMC5712586.
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Front Microbiol. 2017 Nov 29;8:2366. doi: 10.3389/fmicb.2017.02366. eCollection 2017.

Combination of Classifiers Identifies Fungal-Specific Activation of Lysosome Genes in Human Monocytes.

Frontiers in microbiology

João P Leonor Fernandes Saraiva, Cristina Zubiria-Barrera, Tilman E Klassert, Maximilian J Lautenbach, Markus Blaess, Ralf A Claus, Hortense Slevogt, Rainer König

Affiliations

  1. Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany.
  2. Integrated Research and Treatment Center, Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.
  3. Septomics Research Centre, Jena University Hospital, Jena, Germany.

PMID: 29238336 PMCID: PMC5712586 DOI: 10.3389/fmicb.2017.02366

Abstract

Blood stream infections can be caused by several pathogens such as viruses, fungi and bacteria and can cause severe clinical complications including sepsis. Delivery of appropriate and quick treatment is mandatory. However, it requires a rapid identification of the invading pathogen. The current gold standard for pathogen identification relies on blood cultures and these methods require a long time to gain the needed diagnosis. The use of

Keywords: SVM; classification; feature selection; gene expression; machine learning

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