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For: 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]
Number Citing Articles
1 Dahan E, Martin VM, Yassour M. EasyMap - An Interactive Web Tool for Evaluating and Comparing Associations of Clinical Variables and Microbiome Composition. Front Cell Infect Microbiol 2022;12:854164. [DOI: 10.3389/fcimb.2022.854164] [Reference Citation Analysis]
2 Watson DS. Interpretable machine learning for genomics. Hum Genet 2021. [PMID: 34669035 DOI: 10.1007/s00439-021-02387-9] [Reference Citation Analysis]
3 Roder J, Maguire L, Georgantas R 3rd, Roder H. Explaining multivariate molecular diagnostic tests via Shapley values. BMC Med Inform Decis Mak 2021;21:211. [PMID: 34238309 DOI: 10.1186/s12911-021-01569-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Oh S, Kim Y. Machine learning application reveal dynamic interaction of polyphosphate-accumulating organism in full-scale wastewater treatment plant. Journal of Water Process Engineering 2021;44:102417. [DOI: 10.1016/j.jwpe.2021.102417] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Kamal MS, Dey N, Chowdhury L, Hasan SI, Santosh K. Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning. IEEE Trans Instrum Meas 2022;71:1-9. [DOI: 10.1109/tim.2022.3171613] [Reference Citation Analysis]
6 Nagpal S, Singh R, Taneja B, Mande SS. MarkerML – Marker feature identification in metagenomic datasets using interpretable machine learning. Journal of Molecular Biology 2022. [DOI: 10.1016/j.jmb.2022.167589] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Conti F, Frosini P, Quercioli N. On the Construction of Group Equivariant Non-Expansive Operators via Permutants and Symmetric Functions. Front Artif Intell 2022;5:786091. [DOI: 10.3389/frai.2022.786091] [Reference Citation Analysis]
8 Gardiner LJ, Rusholme-Pilcher R, Colmer J, Rees H, Crescente JM, Carrieri AP, Duncan S, Pyzer-Knapp EO, Krishna R, Hall A. Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function. Proc Natl Acad Sci U S A 2021;118:e2103070118. [PMID: 34353905 DOI: 10.1073/pnas.2103070118] [Reference Citation Analysis]
9 Tsuji R, Yazawa K, Kokubo T, Nakamura Y, Kanauchi O. The Effects of Dietary Supplementation of Lactococcus lactis Strain Plasma on Skin Microbiome and Skin Conditions in Healthy Subjects-A Randomized, Double-Blind, Placebo-Controlled Trial. Microorganisms 2021;9:563. [PMID: 33803200 DOI: 10.3390/microorganisms9030563] [Reference Citation Analysis]
10 Murphy B, Hoptroff M, Arnold D, Eccles R, Campbell-Lee S. In-vivo impact of common cosmetic preservative systems in full formulation on the skin microbiome. PLoS One 2021;16:e0254172. [PMID: 34234383 DOI: 10.1371/journal.pone.0254172] [Reference Citation Analysis]
11 McCoubrey LE, Gaisford S, Orlu M, Basit AW. Predicting drug-microbiome interactions with machine learning. Biotechnol Adv 2021;:107797. [PMID: 34260950 DOI: 10.1016/j.biotechadv.2021.107797] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 12.0] [Reference Citation Analysis]