A toolbox of machine learning software to support microbiome analysis.
Marcos-Zambrano LJ, López-Molina VM, Bakir-Gungor B, Frohme M, Karaduzovic-Hadziabdic K, Klammsteiner T, Ibrahimi E, Lahti L, Loncar-Turukalo T, Dhamo X, Simeon A, Nechyporenko A, Pio G, Przymus P, Sampri A, Trajkovik V, Lacruz-Pleguezuelos B, Aasmets O, Araujo R, Anagnostopoulos I, Aydemir Ö, Berland M, Calle ML, Ceci M, Duman H, Gündoğdu A, Havulinna AS, Kaka Bra KHN, Kalluci E, Karav S, Lode D, Lopes MB, May P, Nap B, Nedyalkova M, Paciência I, Pasic L, Pujolassos M, Shigdel R, Susín A, Thiele I, Truică CO, Wilmes P, Yilmaz E, Yousef M, Claesson MJ, Truu J, Carrillo de Santa Pau E.
Marcos-Zambrano LJ, et al.
Front Microbiol. 2023 Nov 22;14:1250806. doi: 10.3389/fmicb.2023.1250806. eCollection 2023.
Front Microbiol. 2023.
PMID: 38075858
Free PMC article.
Review.
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 d …
These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify micro …