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Publication : microbial biotechnology

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Contribution of machine learning for subspecies identification from Mycobacterium abscessus with MALDI-TOF MS in solid and liquid media.

Authors : Godmer A; U1135, Centre d'Immunologie et des Maladies Infectieuses (Cimi-Paris), Sorbonne Université, Paris, France.; AP-HP, Sorbonne Université (Assistance Publique Hôpitaux de Paris), Département de Bactériologie, Groupe Hospitalier Universitaire, Sorbonne Université, Hôpital, Paris, France.

Subjects: Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization*/Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization*/Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization*/methods ; Mycobacterium abscessus*/Mycobacterium abscessus*/Mycobacterium abscessus*/classification ; Mycobacterium abscessus*/Mycobacterium abscessus*/Mycobacterium abscessus*/chemistry

Source: Microbial biotechnology [Microb Biotechnol] 2024 Sep; Vol. 17 (9), pp. e14545.Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 101316335 Publication Model: Print Cited Medium: Internet ISSN:

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Combining comparative genomic analysis with machine learning reveals some promising diagnostic markers to identify five common pathogenic non‐tuberculous mycobacteria.

Authors : Jia, Xinmiao^1,2 (AUTHOR); Yang, Linfang³ (AUTHOR); Li, Cuidan⁴ (AUTHOR)

Subjects: *Genomics; *Mycobacteria; *Machine learning

Source: Microbial Biotechnology. Jul2021, Vol. 14 Issue 4, p1539-1549. 11p.

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