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For: Prifti E, Chevaleyre Y, Hanczar B, Belda E, Danchin A, Clément K, Zucker JD. Interpretable and accurate prediction models for metagenomics data. Gigascience 2020;9:giaa010. [PMID: 32150601 DOI: 10.1093/gigascience/giaa010] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 14.0] [Reference Citation Analysis]
Number Citing Articles
1 Alili R, Belda E, Le P, Wirth T, Zucker JD, Prifti E, Clément K. Exploring Semi-Quantitative Metagenomic Studies Using Oxford Nanopore Sequencing: A Computational and Experimental Protocol. Genes (Basel) 2021;12:1496. [PMID: 34680891 DOI: 10.3390/genes12101496] [Reference Citation Analysis]
2 Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, Gasbarrini A, Tortora G. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 2020;17:635-48. [DOI: 10.1038/s41575-020-0327-3] [Cited by in Crossref: 31] [Cited by in F6Publishing: 25] [Article Influence: 15.5] [Reference Citation Analysis]
3 Prifti E, Chevaleyre Y, Hanczar B, Belda E, Danchin A, Clément K, Zucker JD. Interpretable and accurate prediction models for metagenomics data. Gigascience 2020;9:giaa010. [PMID: 32150601 DOI: 10.1093/gigascience/giaa010] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 14.0] [Reference Citation Analysis]
4 Gordon-Rodriguez E, Quinn TP, Cunningham JP. Learning Sparse Log-Ratios for High-Throughput Sequencing Data. Bioinformatics 2021:btab645. [PMID: 34498030 DOI: 10.1093/bioinformatics/btab645] [Reference Citation Analysis]
5 Cheung H, Yu J. Machine learning on microbiome research in gastrointestinal cancer. J Gastroenterol Hepatol 2021;36:817-22. [PMID: 33880761 DOI: 10.1111/jgh.15502] [Reference Citation Analysis]
6 Carrieri AP, Haiminen N, Maudsley-Barton S, Gardiner LJ, Murphy B, Mayes AE, Paterson S, Grimshaw S, Winn M, Shand C, Hadjidoukas P, Rowe WPM, Hawkins S, MacGuire-Flanagan A, Tazzioli J, Kenny JG, Parida L, Hoptroff M, Pyzer-Knapp EO. Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences. Sci Rep 2021;11:4565. [PMID: 33633172 DOI: 10.1038/s41598-021-83922-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
7 Danchin A, Fenton AA. From Analog to Digital Computing: Is Homo sapiens’ Brain on Its Way to Become a Turing Machine? Front Ecol Evol 2022;10:796413. [DOI: 10.3389/fevo.2022.796413] [Reference Citation Analysis]
8 Zeng T, Yu X, Chen Z. Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review. J Gastroenterol Hepatol 2021;36:832-40. [PMID: 33880762 DOI: 10.1111/jgh.15503] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Kolmeder CA, de Vos WM. Roadmap to functional characterization of the human intestinal microbiota in its interaction with the host. J Pharm Biomed Anal 2021;194:113751. [PMID: 33328144 DOI: 10.1016/j.jpba.2020.113751] [Reference Citation Analysis]
10 [DOI: 10.1101/2021.05.02.442344] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 [DOI: 10.1101/2020.07.02.184713] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Danchin A. In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology. Microb Biotechnol 2021. [PMID: 34570957 DOI: 10.1111/1751-7915.13937] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]