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For: Zhou YH, Gallins P. A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Front Genet. 2019;10:579. [PMID: 31293616 DOI: 10.3389/fgene.2019.00579] [Cited by in Crossref: 50] [Cited by in F6Publishing: 41] [Article Influence: 16.7] [Reference Citation Analysis]
Number Citing Articles
1 Iablokov SN, Klimenko NS, Efimova DA, Shashkova T, Novichkov PS, Rodionov DA, Tyakht AV. Metabolic Phenotypes as Potential Biomarkers for Linking Gut Microbiome With Inflammatory Bowel Diseases. Front Mol Biosci 2020;7:603740. [PMID: 33537340 DOI: 10.3389/fmolb.2020.603740] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
2 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]
3 Mehta SD, Zhao D, Green SJ, Agingu W, Otieno F, Bhaumik R, Bhaumik D, Bailey RC. The Microbiome Composition of a Man's Penis Predicts Incident Bacterial Vaginosis in His Female Sex Partner With High Accuracy. Front Cell Infect Microbiol 2020;10:433. [PMID: 32903746 DOI: 10.3389/fcimb.2020.00433] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
4 Chen X, Liu L, Zhang W, Yang J, Wong KC. Human host status inference from temporal microbiome changes via recurrent neural networks. Brief Bioinform 2021:bbab223. [PMID: 34151933 DOI: 10.1093/bib/bbab223] [Reference Citation Analysis]
5 Eetemadi A, Rai N, Pereira BMP, Kim M, Schmitz H, Tagkopoulos I. The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health. Front Microbiol 2020;11:393. [PMID: 32318028 DOI: 10.3389/fmicb.2020.00393] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
6 Willis JR, Gabaldón T. The Human Oral Microbiome in Health and Disease: From Sequences to Ecosystems. Microorganisms 2020;8:E308. [PMID: 32102216 DOI: 10.3390/microorganisms8020308] [Cited by in Crossref: 51] [Cited by in F6Publishing: 40] [Article Influence: 25.5] [Reference Citation Analysis]
7 Maldonado-Mateus LY, Perez-Burillo S, Lerma-Aguilera A, Hinojosa-Nogueira D, Ruíz-Pérez S, Gosalbes MJ, Francino MP, Rufián-Henares JÁ, Pastoriza de la Cueva S. Effect of roasting conditions on cocoa bioactivity and gut microbiota modulation. Food Funct 2021;12:9680-92. [PMID: 34664589 DOI: 10.1039/d1fo01155c] [Reference Citation Analysis]
8 Li ZM, Zhuang X. Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer. Artif Intell Gastroenterol 2021; 2(4): 105-110 [DOI: 10.35712/aig.v2.i4.105] [Reference Citation Analysis]
9 Bokulich NA, Ziemski M, Robeson MS 2nd, Kaehler BD. Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods. Comput Struct Biotechnol J 2020;18:4048-62. [PMID: 33363701 DOI: 10.1016/j.csbj.2020.11.049] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
10 Lewis S, Nash A, Li Q, Ahn TH. Comparison of 16S and whole genome dog microbiomes using machine learning. BioData Min 2021;14:41. [PMID: 34419136 DOI: 10.1186/s13040-021-00270-x] [Reference Citation Analysis]
11 Maruyama H, Masago A, Nambu T, Mashimo C, Takahashi K, Okinaga T. Inter-site and interpersonal diversity of salivary and tongue microbiomes, and the effect of oral care tablets. F1000Res 2020;9:1477. [DOI: 10.12688/f1000research.27502.1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 García-Jiménez B, Muñoz J, Cabello S, Medina J, Wilkinson MD. Predicting microbiomes through a deep latent space. Bioinformatics 2021;37:1444-51. [PMID: 33289510 DOI: 10.1093/bioinformatics/btaa971] [Reference Citation Analysis]
13 Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, Bork P, Sunagawa S, Zeller G. Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox. Genome Biol 2021;22:93. [PMID: 33785070 DOI: 10.1186/s13059-021-02306-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
14 Egli A, Schrenzel J, Greub G. Digital microbiology. Clin Microbiol Infect 2020;26:1324-31. [PMID: 32603804 DOI: 10.1016/j.cmi.2020.06.023] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Iadanza E, Fabbri R, Bašić-čičak D, Amedei A, Telalovic JH. Gut microbiota and artificial intelligence approaches: A scoping review. Health Technol 2020;10:1343-58. [DOI: 10.1007/s12553-020-00486-7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
16 Leggieri PA, Liu Y, Hayes M, Connors B, Seppälä S, O'Malley MA, Venturelli OS. Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes. Annu Rev Biomed Eng 2021;23:169-201. [PMID: 33781078 DOI: 10.1146/annurev-bioeng-082120-022836] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 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]
18 Mallick H, Alhamzawi R, Paul E, Svetnik V. The reciprocal Bayesian LASSO. Stat Med 2021. [PMID: 34126655 DOI: 10.1002/sim.9098] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Zhou H, He K, Chen J, Zhang X. LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol 2022;23. [DOI: 10.1186/s13059-022-02655-5] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Deng W, Dittoe DK, Pavilidis HO, Chaney WE, Yang Y, Ricke SC. Current Perspectives and Potential of Probiotics to Limit Foodborne Campylobacter in Poultry. Front Microbiol 2020;11:583429. [PMID: 33414767 DOI: 10.3389/fmicb.2020.583429] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
21 Song K, Wright FA, Zhou YH. Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction. Front Mol Biosci 2020;7:610845. [PMID: 33392266 DOI: 10.3389/fmolb.2020.610845] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Maruyama H, Masago A, Nambu T, Mashimo C, Takahashi K, Okinaga T. Inter-site and interpersonal diversity of salivary and tongue microbiomes, and the effect of oral care tablets. F1000Res 2020;9:1477. [PMID: 33732447 DOI: 10.12688/f1000research.27502.2] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
23 Choi Y, Kim K, Kim S, Kim D. Identification of odor emission sources in urban areas using machine learning-based classification models. Atmospheric Environment: X 2022;13:100156. [DOI: 10.1016/j.aeaoa.2022.100156] [Reference Citation Analysis]
24 Kumar P, Sinha R, Shukla P. Artificial intelligence and synthetic biology approaches for human gut microbiome. Crit Rev Food Sci Nutr 2020;:1-19. [PMID: 33249867 DOI: 10.1080/10408398.2020.1850415] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
25 Liu B, Sträuber H, Saraiva J, Harms H, Silva SG, Kasmanas JC, Kleinsteuber S, Nunes da Rocha U. Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture. Microbiome 2022;10:48. [PMID: 35331330 DOI: 10.1186/s40168-021-01219-2] [Reference Citation Analysis]
26 Miao R, Badger TC, Groesch K, Diaz-Sylvester PL, Wilson T, Ghareeb A, Martin JA, Cregger M, Welge M, Bushell C, Auvil L, Zhu R, Brard L, Braundmeier-Fleming A. Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer. PLoS One 2020;15:e0227707. [PMID: 31917801 DOI: 10.1371/journal.pone.0227707] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
27 Comin M, Di Camillo B, Pizzi C, Vandin F. Comparison of microbiome samples: methods and computational challenges. Brief Bioinform 2021;22:88-95. [PMID: 32577746 DOI: 10.1093/bib/bbaa121] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
28 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]
29 Shinn LM, Li Y, Mansharamani A, Auvil LS, Welge ME, Bushell C, Khan NA, Charron CS, Novotny JA, Baer DJ, Zhu R, Holscher HD. Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults. J Nutr 2021;151:423-33. [PMID: 33021315 DOI: 10.1093/jn/nxaa285] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
30 Roesch LFW, Dobbler PT, Pylro VS, Kolaczkowski B, Drew JC, Triplett EW. pime : A package for discovery of novel differences among microbial communities. Mol Ecol Resour 2020;20:415-28. [DOI: 10.1111/1755-0998.13116] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
31 Ramon E, Belanche-Muñoz L, Molist F, Quintanilla R, Perez-Enciso M, Ramayo-Caldas Y. kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets. Front Microbiol 2021;12:609048. [PMID: 33584612 DOI: 10.3389/fmicb.2021.609048] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
32 Nguyen QP, Karagas MR, Madan JC, Dade E, Palys TJ, Morrison HG, Pathmasiri WW, McRitche S, Sumner SJ, Frost HR, Hoen AG. Associations between the gut microbiome and metabolome in early life. BMC Microbiol 2021;21:238. [PMID: 34454437 DOI: 10.1186/s12866-021-02282-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
33 McCoubrey LE, Elbadawi M, Orlu M, Gaisford S, Basit AW. Harnessing machine learning for development of microbiome therapeutics. Gut Microbes 2021;13:1-20. [PMID: 33522391 DOI: 10.1080/19490976.2021.1872323] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 11.0] [Reference Citation Analysis]
34 Kaur H, Singh Y, Singh S, Singh RB. Gut microbiome-mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis. Genome 2021;64:355-71. [PMID: 33031715 DOI: 10.1139/gen-2020-0136] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
35 [DOI: 10.1101/2020.07.02.184713] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
36 Søndertoft NB, Vogt JK, Arumugam M, Kristensen M, Gøbel RJ, Fan Y, Lyu L, Bahl MI, Eriksen C, Ängquist L, Frøkiær H, Hansen TH, Brix S, Nielsen HB, Hansen T, Vestergaard H, Gupta R, Licht TR, Lauritzen L, Pedersen O. The intestinal microbiome is a co-determinant of the postprandial plasma glucose response. PLoS One 2020;15:e0238648. [PMID: 32947608 DOI: 10.1371/journal.pone.0238648] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
37 Ahmed E, Hens K. Microbiome in Precision Psychiatry: An Overview of the Ethical Challenges Regarding Microbiome Big Data and Microbiome-Based Interventions. AJOB Neurosci 2021;:1-17. [PMID: 34379050 DOI: 10.1080/21507740.2021.1958096] [Reference Citation Analysis]
38 Tsiknia M, Tsikou D, Papadopoulou KK, Ehaliotis C. Multi-species relationships in legume roots: From pairwise legume-symbiont interactions to the plant - microbiome - soil continuum. FEMS Microbiol Ecol 2021;97:fiaa222. [PMID: 33155054 DOI: 10.1093/femsec/fiaa222] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
39 David MM, Tataru C, Pope Q, Baker LJ, English MK, Epstein HE, Hammer A, Kent M, Sieler MJ Jr, Mueller RS, Sharpton TJ, Tomas F, Vega Thurber R, Fern XZ. Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning. mSystems 2022;:e0105821. [PMID: 35040699 DOI: 10.1128/msystems.01058-21] [Reference Citation Analysis]
40 Pérez-Cobas AE, Gomez-Valero L, Buchrieser C. Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses. Microb Genom 2020;6. [PMID: 32706331 DOI: 10.1099/mgen.0.000409] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 5.5] [Reference Citation Analysis]
41 Deng Z, Zhang J, Li J, Zhang X. Application of Deep Learning in Plant-Microbiota Association Analysis. Front Genet 2021;12:697090. [PMID: 34691142 DOI: 10.3389/fgene.2021.697090] [Reference Citation Analysis]
42 Ghannam RB, Techtmann SM. Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring. Comput Struct Biotechnol J 2021;19:1092-107. [PMID: 33680353 DOI: 10.1016/j.csbj.2021.01.028] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
43 Minich JJ, Poore GD, Jantawongsri K, Johnston C, Bowie K, Bowman J, Knight R, Nowak B, Allen EE. Microbial Ecology of Atlantic Salmon (Salmo salar) Hatcheries: Impacts of the Built Environment on Fish Mucosal Microbiota. Appl Environ Microbiol 2020;86:e00411-20. [PMID: 32303543 DOI: 10.1128/AEM.00411-20] [Cited by in Crossref: 18] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
44 Na HS, Kim SY, Han H, Kim HJ, Lee JY, Lee JH, Chung J. Identification of Potential Oral Microbial Biomarkers for the Diagnosis of Periodontitis. J Clin Med 2020;9:E1549. [PMID: 32443919 DOI: 10.3390/jcm9051549] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
45 Zhao Y, Federico A, Faits T, Manimaran S, Segrè D, Monti S, Johnson WE. animalcules: interactive microbiome analytics and visualization in R. Microbiome 2021;9:76. [PMID: 33775256 DOI: 10.1186/s40168-021-01013-0] [Reference Citation Analysis]
46 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]
47 Zhang W, Chen X, Wong KC. Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome. J Gastroenterol Hepatol 2021;36:823-31. [PMID: 33880763 DOI: 10.1111/jgh.15500] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Parvandeh S, Donehower LA, Panagiotis K, Hsu TK, Asmussen JK, Lee K, Lichtarge O. EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants. Nucleic Acids Res 2022:gkac215. [PMID: 35412634 DOI: 10.1093/nar/gkac215] [Reference Citation Analysis]
49 Wilhelm RC, van Es HM, Buckley DH. Predicting measures of soil health using the microbiome and supervised machine learning. Soil Biology and Biochemistry 2022;164:108472. [DOI: 10.1016/j.soilbio.2021.108472] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
50 Arbet J, Brokamp C, Meinzen-Derr J, Trinkley KE, Spratt HM. Lessons and tips for designing a machine learning study using EHR data. J Clin Transl Sci 2020;5:e21. [PMID: 33948244 DOI: 10.1017/cts.2020.513] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
51 Gao K, Mei G, Piccialli F, Cuomo S, Tu J, Huo Z. Julia language in machine learning: Algorithms, applications, and open issues. Computer Science Review 2020;37:100254. [DOI: 10.1016/j.cosrev.2020.100254] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]