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For: Goodswen SJ, Barratt JLN, Kennedy PJ, Kaufer A, Calarco L, Ellis JT. Machine learning and applications in microbiology. FEMS Microbiol Rev 2021:fuab015. [PMID: 33724378 DOI: 10.1093/femsre/fuab015] [Cited by in Crossref: 2] [Cited by in F6Publishing: 19] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang H, Zhang W, Tang YW. Clinical Microbiology in Detection and Identification of Emerging Microbial Pathogens: Past, Present and Future. Emerg Microbes Infect 2022;:1-27. [PMID: 36121351 DOI: 10.1080/22221751.2022.2125345] [Reference Citation Analysis]
2 Shi Z, Wei L, Wang P, Wang S, Liu Z, Jiang Y, Wang J. Spatio-temporal spread and evolution of influenza A (H7N9) viruses. Front Microbiol 2022;13:1002522. [DOI: 10.3389/fmicb.2022.1002522] [Reference Citation Analysis]
3 Ge S, Xu C, Li Y, Zhang Y, Li N, Wang F, Ding L, Niu J, Shi Z. Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm. Journal of Oncology 2022;2022:1-13. [DOI: 10.1155/2022/2469592] [Reference Citation Analysis]
4 De Farias Silva CE, Costa GYSCM, Ferro JV, de Oliveira Carvalho F, da Gama BMV, Meili L, dos Santos Silva MC, Almeida RMRG, Tonholo J. Application of machine learning to predict the yield of alginate lyase solid-state fermentation by Cunninghamella echinulata: artificial neural networks and support vector machine. Reac Kinet Mech Cat. [DOI: 10.1007/s11144-022-02293-9] [Reference Citation Analysis]
5 Comas I, Moreno-Molina M. Phenogenomics of Mycobacterium abscessus. Nat Microbiol 2022;7:1325-6. [PMID: 36008618 DOI: 10.1038/s41564-022-01217-6] [Reference Citation Analysis]
6 Jacobson D, Zheng Y, Plucinski MM, Qvarnstrom Y, Barratt JLN. Evaluation of various distance computation methods for construction of haplotype-based phylogenies from large MLST dataset. Mol Phylogenet Evol 2022;177:107608. [PMID: 35963590 DOI: 10.1016/j.ympev.2022.107608] [Reference Citation Analysis]
7 de la Haba RR, Antunes A, Hedlund BP. Editorial: Extremophiles: Microbial genomics and taxogenomics. Front Microbiol 2022;13:984632. [DOI: 10.3389/fmicb.2022.984632] [Reference Citation Analysis]
8 Pei Z, Liu S, Jing Z, Zhang Y, Wang J, Liu J, Wang Y, Guo W, Li Y, Feng L, Zhou H, Li G, Han Y, Liu D, Pan J. Understanding of the interrelationship between methane production and microorganisms in high-solid anaerobic co-digestion using microbial analysis and machine learning. Journal of Cleaner Production 2022. [DOI: 10.1016/j.jclepro.2022.133848] [Reference Citation Analysis]
9 Worth RM, Espina L. ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth. Front Microbiol 2022;13:900596. [DOI: 10.3389/fmicb.2022.900596] [Reference Citation Analysis]
10 Møller TE, Le Moine Bauer S, Hannisdal B, Zhao R, Baumberger T, Roerdink DL, Dupuis A, Thorseth IH, Pedersen RB, Jørgensen SL. Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance. Front Microbiol 2022;13:804575. [PMID: 35663876 DOI: 10.3389/fmicb.2022.804575] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Priyadharshini T, Nageshwari K, Vimaladhasan S, Parag Prakash S, Balasubramanian P. Machine learning prediction of SCOBY cellulose yield from Kombucha tea fermentation. Bioresource Technology Reports 2022;18:101027. [DOI: 10.1016/j.biteb.2022.101027] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 McElhinney JMWR, Catacutan MK, Mawart A, Hasan A, Dias J. Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges. Front Microbiol 2022;13:851450. [PMID: 35547145 DOI: 10.3389/fmicb.2022.851450] [Reference Citation Analysis]
13 Hu R, Hesham AE, Zou Q. Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases. Front Cell Infect Microbiol 2022;12:882995. [DOI: 10.3389/fcimb.2022.882995] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Blatt SH, Shields JR, Michael AR. Dental Calculus Reveals Life History of Decedents in Forensic Cases: An Anthropological Perspective on Human Identification. Forensic Genomics 2022;2:5-16. [DOI: 10.1089/forensic.2022.0003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Feucherolles M, Nennig M, Becker SL, Martiny D, Losch S, Penny C, Cauchie H, Ragimbeau C. Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case of Campylobacter spp. Front Microbiol 2022;12:804484. [DOI: 10.3389/fmicb.2021.804484] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Aragaki H, Ogoh K, Kondo Y, Aoki K. LIM Tracker: a software package for cell tracking and analysis with advanced interactivity. Sci Rep 2022;12:2702. [PMID: 35177675 DOI: 10.1038/s41598-022-06269-6] [Reference Citation Analysis]
17 Özel Duygan BD, van der Meer JR. Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data. Curr Opin Biotechnol 2022;75:102688. [PMID: 35123235 DOI: 10.1016/j.copbio.2022.102688] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
18 John Cremin C, Dash S, Huang X. Big Data: Historic Advances and Emerging Trends in Biomedical Research. Current Research in Biotechnology 2022. [DOI: 10.1016/j.crbiot.2022.02.004] [Reference Citation Analysis]
19 DiMucci D, Kon M, Segrè D. BowSaw: Inferring Higher-Order Trait Interactions Associated With Complex Biological Phenotypes. Front Mol Biosci 2021;8:663532. [PMID: 34222331 DOI: 10.3389/fmolb.2021.663532] [Reference Citation Analysis]