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Cited by in F6Publishing
For: Fabijanić M, Vlahoviček K. Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles. In: Carugo O, Eisenhaber F, editors. Data Mining Techniques for the Life Sciences. New York: Springer; 2016. pp. 509-31. [DOI: 10.1007/978-1-4939-3572-7_26] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
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2 Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lopes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Microbiol 2021;12:634511. [PMID: 33737920 DOI: 10.3389/fmicb.2021.634511] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 10.0] [Reference Citation Analysis]
3 Blanco-Míguez A, Fdez-Riverola F, Sánchez B, Lourenço A. Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. Brief Bioinform 2019;20:1032-56. [PMID: 29186315 DOI: 10.1093/bib/bbx156] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]