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For: Topçuoğlu BD, Lesniak NA, Ruffin MT 4th, Wiens J, Schloss PD. A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems. mBio 2020;11:e00434-20. [PMID: 32518182 DOI: 10.1128/mBio.00434-20] [Cited by in Crossref: 26] [Cited by in F6Publishing: 14] [Article Influence: 13.0] [Reference Citation Analysis]
Number Citing Articles
1 Matteoli FP, Silva AM, Feiler HP, de Araújo VL, Cardoso EJ. Predicting soil farming system and attributes based on soil bacterial community. Applied Soil Ecology 2022;171:104335. [DOI: 10.1016/j.apsoil.2021.104335] [Reference Citation Analysis]
2 Lesniak NA, Schubert AM, Sinani H, Schloss PD. Clearance of Clostridioides difficile Colonization Is Associated with Antibiotic-Specific Bacterial Changes. mSphere 2021;6:e01238-20. [PMID: 33952668 DOI: 10.1128/mSphere.01238-20] [Reference Citation Analysis]
3 Tomkovich S, Stough JMA, Bishop L, Schloss PD. The Initial Gut Microbiota and Response to Antibiotic Perturbation Influence Clostridioides difficile Clearance in Mice. mSphere 2020;5:e00869-20. [PMID: 33087520 DOI: 10.1128/mSphere.00869-20] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
4 Barron MR, Young VB. Viewing Bacterial Colonization through the Lens of Systems Biology. mSystems 2022;:e0138321. [PMID: 35354321 DOI: 10.1128/msystems.01383-21] [Reference Citation Analysis]
5 Kubinski R, Djamen-kepaou J, Zhanabaev T, Hernandez-garcia A, Bauer S, Hildebrand F, Korcsmaros T, Karam S, Jantchou P, Kafi K, Martin RD. Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease. Front Genet 2022;13:784397. [DOI: 10.3389/fgene.2022.784397] [Reference Citation Analysis]
6 Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B, Santa Pau EC, D'Elia D, Desai MS, Falquet L, Gundogdu A, Hron K, Klammsteiner T, Lopes MB, Marcos-Zambrano LJ, Marques C, Mason M, May P, Pašić L, Pio G, Pongor S, Promponas VJ, Przymus P, Saez-Rodriguez J, Sampri A, Shigdel R, Stres B, Suharoschi R, Truu J, Truică CO, Vilne B, Vlachakis D, Yilmaz E, Zeller G, Zomer AL, Gómez-Cabrero D, Claesson MJ. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front Microbiol 2021;12:635781. [PMID: 33692771 DOI: 10.3389/fmicb.2021.635781] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
7 Wu S, Chen Y, Li Z, Li J, Zhao F, Su X. Towards multi-label classification: Next step of machine learning for microbiome research. Comput Struct Biotechnol J 2021;19:2742-9. [PMID: 34093989 DOI: 10.1016/j.csbj.2021.04.054] [Reference Citation Analysis]
8 Zhang P, Wang Z, Qiu H, Zhou W, Wang M, Cheng G. Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis. Comput Struct Biotechnol J 2021;19:3284-92. [PMID: 34188777 DOI: 10.1016/j.csbj.2021.05.024] [Reference Citation Analysis]
9 Dumont-Leblond N, Veillette M, Racine C, Joubert P, Duchaine C. Non-small cell lung cancer microbiota characterization: Prevalence of enteric and potentially pathogenic bacteria in cancer tissues. PLoS One 2021;16:e0249832. [PMID: 33891617 DOI: 10.1371/journal.pone.0249832] [Reference Citation Analysis]
10 Caddey B, De Buck J. Meta-Analysis of Bovine Digital Dermatitis Microbiota Reveals Distinct Microbial Community Structures Associated With Lesions. Front Cell Infect Microbiol 2021;11:685861. [PMID: 34336713 DOI: 10.3389/fcimb.2021.685861] [Reference Citation Analysis]
11 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]
12 Chen Y, Wang H, Lu W, Wu T, Yuan W, Zhu J, Lee YK, Zhao J, Zhang H, Chen W. Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning. Gut Microbes 2022;14:2025016. [PMID: 35040752 DOI: 10.1080/19490976.2021.2025016] [Reference Citation Analysis]
13 Mansour RF, Alfar NM, Abdel‐khalek S, Abdelhaq M, Saeed RA, Alsaqour R. Optimal deep learning based fusion model for biomedical image classification. Expert Systems. [DOI: 10.1111/exsy.12764] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Poudel M, Mendes R, Costa LAS, Bueno CG, Meng Y, Folimonova SY, Garrett KA, Martins SJ. The Role of Plant-Associated Bacteria, Fungi, and Viruses in Drought Stress Mitigation. Front Microbiol 2021;12:743512. [PMID: 34759901 DOI: 10.3389/fmicb.2021.743512] [Reference Citation Analysis]
15 Kim Y, Park S, Oh S. Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup. Microorganisms 2021;9:1387. [PMID: 34202381 DOI: 10.3390/microorganisms9071387] [Reference Citation Analysis]
16 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]
17 Tomkovich S, Taylor A, King J, Colovas J, Bishop L, McBride K, Royzenblat S, Lesniak NA, Bergin IL, Schloss PD. An Osmotic Laxative Renders Mice Susceptible to Prolonged Clostridioides difficile Colonization and Hinders Clearance. mSphere 2021;6:e0062921. [PMID: 34585964 DOI: 10.1128/mSphere.00629-21] [Reference Citation Analysis]
18 Islam J, Tanimizu M, Shimizu Y, Goto Y, Ohtani N, Sugiyama K, Tatezaki E, Sato M, Makino E, Shimada T, Ueda C, Matsuo A, Suyama Y, Sakai Y, Furukawa M, Usami K, Yoneyama H, Aso H, Tanaka H, Nochi T. Development of a rational framework for the therapeutic efficacy of fecal microbiota transplantation for calf diarrhea treatment. Microbiome 2022;10. [DOI: 10.1186/s40168-021-01217-4] [Reference Citation Analysis]
19 Kim Y, Oh S. Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant. J Environ Manage 2021;300:113795. [PMID: 34560468 DOI: 10.1016/j.jenvman.2021.113795] [Reference Citation Analysis]
20 Volkova A, Ruggles KV. Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures. Front Microbiol 2021;12:621310. [PMID: 33746917 DOI: 10.3389/fmicb.2021.621310] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
21 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]
22 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]
23 Hagan AK, Topçuoğlu BD, Gregory ME, Barton HA, Schloss PD. Women Are Underrepresented and Receive Differential Outcomes at ASM Journals: a Six-Year Retrospective Analysis. mBio 2020;11:e01680-20. [PMID: 33262256 DOI: 10.1128/mBio.01680-20] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
24 Topçuoğlu BD, Lapp Z, Sovacool KL, Snitkin E, Wiens J, Schloss PD. mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines. J Open Source Softw 2021;6:3073. [PMID: 34414351 DOI: 10.21105/joss.03073] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
25 Busa J, Polaka I. Variability of Classification Results in Data with High Dimensionality and Small Sample Size. ITMS 2021;24:45-52. [DOI: 10.7250/itms-2021-0007] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Yang F, Zou Q. mAML: an automated machine learning pipeline with a microbiome repository for human disease classification. Database (Oxford) 2020;2020:baaa050. [PMID: 32588040 DOI: 10.1093/database/baaa050] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]