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For: Pasolli E, Truong DT, Malik F, Waldron L, Segata N. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLoS Comput Biol. 2016;12:e1004977. [PMID: 27400279 DOI: 10.1371/journal.pcbi.1004977] [Cited by in Crossref: 215] [Cited by in F6Publishing: 156] [Article Influence: 35.8] [Reference Citation Analysis]
Number Citing Articles
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5 Arukha AP, Freguia CF, Mishra M, Jha JK, Kariyawasam S, Fanger NA, Zimmermann EM, Fanger GR, Sahay B. Lactococcus lactis Delivery of Surface Layer Protein A Protects Mice from Colitis by Re-Setting Host Immune Repertoire. Biomedicines 2021;9:1098. [PMID: 34572293 DOI: 10.3390/biomedicines9091098] [Reference Citation Analysis]
6 Jiao N, Loomba R, Yang ZH, Wu D, Fang S, Bettencourt R, Lan P, Zhu R, Zhu L. Alterations in bile acid metabolizing gut microbiota and specific bile acid genes as a precision medicine to subclassify NAFLD. Physiol Genomics 2021;53:336-48. [PMID: 34151600 DOI: 10.1152/physiolgenomics.00011.2021] [Reference Citation Analysis]
7 Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, Leeming E, Gibson R, Le Roy C, Khatib HA, Francis L, Mazidi M, Mompeo O, Valles-Colomer M, Tett A, Beghini F, Dubois L, Bazzani D, Thomas AM, Mirzayi C, Khleborodova A, Oh S, Hine R, Bonnett C, Capdevila J, Danzanvilliers S, Giordano F, Geistlinger L, Waldron L, Davies R, Hadjigeorgiou G, Wolf J, Ordovás JM, Gardner C, Franks PW, Chan AT, Huttenhower C, Spector TD, Segata N. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med 2021;27:321-32. [PMID: 33432175 DOI: 10.1038/s41591-020-01183-8] [Cited by in Crossref: 44] [Cited by in F6Publishing: 43] [Article Influence: 44.0] [Reference Citation Analysis]
8 Elgart M, Redline S, Sofer T. Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research. Neurotherapeutics 2021;18:228-43. [PMID: 33829409 DOI: 10.1007/s13311-021-01014-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Kohli A, Holzwanger EA, Levy AN. Emerging use of artificial intelligence in inflammatory bowel disease. World J Gastroenterol 2020; 26(44): 6923-6928 [PMID: 33311940 DOI: 10.3748/wjg.v26.i44.6923] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
10 Lee KA, Thomas AM, Bolte LA, Björk JR, de Ruijter LK, Armanini F, Asnicar F, Blanco-Miguez A, Board R, Calbet-Llopart N, Derosa L, Dhomen N, Brooks K, Harland M, Harries M, Leeming ER, Lorigan P, Manghi P, Marais R, Newton-Bishop J, Nezi L, Pinto F, Potrony M, Puig S, Serra-Bellver P, Shaw HM, Tamburini S, Valpione S, Vijay A, Waldron L, Zitvogel L, Zolfo M, de Vries EGE, Nathan P, Fehrmann RSN, Bataille V, Hospers GAP, Spector TD, Weersma RK, Segata N. Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. Nat Med 2022;28:535-44. [PMID: 35228751 DOI: 10.1038/s41591-022-01695-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 [DOI: 10.1101/2020.11.19.388223] [Cited by in Crossref: 21] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
12 Tierney BT, Tan Y, Kostic AD, Patel CJ. Gene-level metagenomic architectures across diseases yield high-resolution microbiome diagnostic indicators. Nat Commun 2021;12:2907. [PMID: 34006865 DOI: 10.1038/s41467-021-23029-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
13 Bang S, Yoo D, Kim SJ, Jhang S, Cho S, Kim H. Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data. Sci Rep 2019;9:10189. [PMID: 31308384 DOI: 10.1038/s41598-019-46249-x] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
14 van den Bogert B, Boekhorst J, Pirovano W, May A. On the Role of Bioinformatics and Data Science in Industrial Microbiome Applications. Front Genet 2019;10:721. [PMID: 31447883 DOI: 10.3389/fgene.2019.00721] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
15 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]
16 Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15:20170387. [PMID: 29618526 DOI: 10.1098/rsif.2017.0387] [Cited by in Crossref: 624] [Cited by in F6Publishing: 412] [Article Influence: 208.0] [Reference Citation Analysis]
17 Vänni P, Tejesvi MV, Ainonen S, Renko M, Korpela K, Salo J, Paalanne N, Tapiainen T. Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome. Sci Rep 2021;11:17483. [PMID: 34471207 DOI: 10.1038/s41598-021-97007-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Waldron L. Data and Statistical Methods To Analyze the Human Microbiome. mSystems 2018;3:e00194-17. [PMID: 29556541 DOI: 10.1128/mSystems.00194-17] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
19 Wang Y, Bhattacharya T, Jiang Y, Qin X, Wang Y, Liu Y, Saykin AJ, Chen L. A novel deep learning method for predictive modeling of microbiome data. Brief Bioinform 2021;22:bbaa073. [PMID: 32406914 DOI: 10.1093/bib/bbaa073] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Bakir-Gungor B, Bulut O, Jabeer A, Nalbantoglu OU, Yousef M. Discovering Potential Taxonomic Biomarkers of Type 2 Diabetes From Human Gut Microbiota via Different Feature Selection Methods. Front Microbiol 2021;12:628426. [PMID: 34512559 DOI: 10.3389/fmicb.2021.628426] [Reference Citation Analysis]
21 Le Goallec A, Tierney BT, Luber JM, Cofer EM, Kostic AD, Patel CJ. A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type. PLoS Comput Biol 2020;16:e1007895. [PMID: 32392251 DOI: 10.1371/journal.pcbi.1007895] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
22 Trivieri N, Pracella R, Cariglia MG, Panebianco C, Parrella P, Visioli A, Giani F, Soriano AA, Barile C, Canistro G, Latiano TP, Dimitri L, Bazzocchi F, Cassano D, Vescovi AL, Pazienza V, Binda E. BRAFV600E mutation impinges on gut microbial markers defining novel biomarkers for serrated colorectal cancer effective therapies. J Exp Clin Cancer Res 2020;39:285. [PMID: 33317591 DOI: 10.1186/s13046-020-01801-w] [Reference Citation Analysis]
23 Ghaffari P, Shoaie S, Nielsen LK. Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions. J Transl Med 2022;20. [DOI: 10.1186/s12967-022-03365-z] [Reference Citation Analysis]
24 Vidulin V, Šmuc T, Džeroski S, Supek F. The evolutionary signal in metagenome phyletic profiles predicts many gene functions. Microbiome 2018;6:129. [PMID: 29991352 DOI: 10.1186/s40168-018-0506-4] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
25 Kumar P, Rani A, Singh S, Kumar A. Recent advances on DNA and omics‐based technology in Food testing and authentication: A review. Journal of Food Safety. [DOI: 10.1111/jfs.12986] [Reference Citation Analysis]
26 Curry KD, Nute MG, Treangen TJ. It takes guts to learn: machine learning techniques for disease detection from the gut microbiome. Emerg Top Life Sci 2021;5:815-27. [PMID: 34779841 DOI: 10.1042/ETLS20210213] [Reference Citation Analysis]
27 Johns MS, Petrelli NJ. Microbiome and colorectal cancer: A review of the past, present, and future. Surg Oncol 2021;37:101560. [PMID: 33848761 DOI: 10.1016/j.suronc.2021.101560] [Reference Citation Analysis]
28 Asgari E, Garakani K, McHardy AC, Mofrad MRK. MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples. Bioinformatics 2018;34:i32-42. [PMID: 29950008 DOI: 10.1093/bioinformatics/bty296] [Cited by in Crossref: 28] [Cited by in F6Publishing: 20] [Article Influence: 9.3] [Reference Citation Analysis]
29 Estaki M, Jiang L, Bokulich NA, McDonald D, González A, Kosciolek T, Martino C, Zhu Q, Birmingham A, Vázquez-Baeza Y, Dillon MR, Bolyen E, Caporaso JG, Knight R. QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data. Curr Protoc Bioinformatics 2020;70:e100. [PMID: 32343490 DOI: 10.1002/cpbi.100] [Cited by in Crossref: 20] [Cited by in F6Publishing: 17] [Article Influence: 20.0] [Reference Citation Analysis]
30 Xia Y. Correlation and association analyses in microbiome study integrating multiomics in health and disease. Prog Mol Biol Transl Sci 2020;171:309-491. [PMID: 32475527 DOI: 10.1016/bs.pmbts.2020.04.003] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
31 Shi K, Zhang L, Yu J, Chen Z, Lai S, Zhao X, Li WG, Luo Q, Lin W, Feng J, Bork P, Zhao XM, Li F. A 12-genus bacterial signature identifies a group of severe autistic children with differential sensory behavior and brain structures. Clin Transl Med 2021;11:e314. [PMID: 33634969 DOI: 10.1002/ctm2.314] [Reference Citation Analysis]
32 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]
33 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]
34 Aasmets O, Lüll K, Lang JM, Pan C, Kuusisto J, Fischer K, Laakso M, Lusis AJ, Org E. Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation. mSystems 2021;6:e01191-20. [PMID: 33594006 DOI: 10.1128/mSystems.01191-20] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
35 Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, Brochado AR, Fernandez KC, Dose H, Mori H, Patil KR, Bork P, Typas A. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature. 2018;555:623-628. [PMID: 29555994 DOI: 10.1038/nature25979] [Cited by in Crossref: 709] [Cited by in F6Publishing: 602] [Article Influence: 177.3] [Reference Citation Analysis]
36 Naseri M, Palizban F, Yadegar A, Khodarahmi M, Asadzadeh Aghdaei H, Houri H, Zahiri J. Investigation and characterization of human gut phageome in advanced liver cirrhosis of defined etiologies. Gut Pathog 2022;14:9. [PMID: 35168645 DOI: 10.1186/s13099-022-00482-4] [Reference Citation Analysis]
37 Gharaibeh RZ, Jobin C. Microbiota and cancer immunotherapy: in search of microbial signals. Gut 2019;68:385-8. [PMID: 30530851 DOI: 10.1136/gutjnl-2018-317220] [Cited by in Crossref: 34] [Cited by in F6Publishing: 28] [Article Influence: 8.5] [Reference Citation Analysis]
38 Levade I, Saber MM, Midani FS, Chowdhury F, Khan AI, Begum YA, Ryan ET, David LA, Calderwood SB, Harris JB, LaRocque RC, Qadri F, Shapiro BJ, Weil AA. Predicting Vibrio cholerae Infection and Disease Severity Using Metagenomics in a Prospective Cohort Study. J Infect Dis 2021;223:342-51. [PMID: 32610345 DOI: 10.1093/infdis/jiaa358] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
39 Tett A, Pasolli E, Farina S, Truong DT, Asnicar F, Zolfo M, Beghini F, Armanini F, Jousson O, De Sanctis V, Bertorelli R, Girolomoni G, Cristofolini M, Segata N. Unexplored diversity and strain-level structure of the skin microbiome associated with psoriasis. NPJ Biofilms Microbiomes 2017;3:14. [PMID: 28649415 DOI: 10.1038/s41522-017-0022-5] [Cited by in Crossref: 83] [Cited by in F6Publishing: 67] [Article Influence: 16.6] [Reference Citation Analysis]
40 Mreyoud Y, Song M, Lim J, Ahn T. MegaD: Deep Learning for Rapid and Accurate Disease Status Prediction of Metagenomic Samples. Life 2022;12:669. [DOI: 10.3390/life12050669] [Reference Citation Analysis]
41 Metcalf JL, Xu ZZ, Bouslimani A, Dorrestein P, Carter DO, Knight R. Microbiome Tools for Forensic Science. Trends Biotechnol 2017;35:814-23. [PMID: 28366290 DOI: 10.1016/j.tibtech.2017.03.006] [Cited by in Crossref: 54] [Cited by in F6Publishing: 37] [Article Influence: 10.8] [Reference Citation Analysis]
42 Maltecca C, Lu D, Schillebeeckx C, McNulty NP, Schwab C, Shull C, Tiezzi F. Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms. Sci Rep 2019;9:6574. [PMID: 31024050 DOI: 10.1038/s41598-019-43031-x] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 4.3] [Reference Citation Analysis]
43 Nishimura N, Kaji K, Kitagawa K, Sawada Y, Furukawa M, Ozutsumi T, Fujinaga Y, Tsuji Y, Takaya H, Kawaratani H, Moriya K, Namisaki T, Akahane T, Fukui H, Yoshiji H. Intestinal Permeability Is a Mechanical Rheostat in the Pathogenesis of Liver Cirrhosis. Int J Mol Sci 2021;22:6921. [PMID: 34203178 DOI: 10.3390/ijms22136921] [Reference Citation Analysis]
44 Raza S, Kim J, Sadowsky MJ, Unno T. Microbial source tracking using metagenomics and other new technologies. J Microbiol 2021;59:259-69. [DOI: 10.1007/s12275-021-0668-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
45 [DOI: 10.1145/3233547.3233585] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]
46 Watson RL, de Koff EM, Bogaert D. Characterising the respiratory microbiome. Eur Respir J 2019;53:1801711. [PMID: 30487204 DOI: 10.1183/13993003.01711-2018] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 3.7] [Reference Citation Analysis]
47 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]
48 Dhungel E, Mreyoud Y, Gwak HJ, Rajeh A, Rho M, Ahn TH. MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning. BMC Bioinformatics 2021;22:25. [PMID: 33461494 DOI: 10.1186/s12859-020-03933-4] [Reference Citation Analysis]
49 Beghini F, Pasolli E, Truong TD, Putignani L, Cacciò SM, Segata N. Large-scale comparative metagenomics of Blastocystis, a common member of the human gut microbiome. ISME J 2017;11:2848-63. [PMID: 28837129 DOI: 10.1038/ismej.2017.139] [Cited by in Crossref: 57] [Cited by in F6Publishing: 59] [Article Influence: 11.4] [Reference Citation Analysis]
50 Ong IM, Gonzalez JG, McIlwain SJ, Sawin EA, Schoen AJ, Adluru N, Alexander AL, Yu JJ. Gut microbiome populations are associated with structure-specific changes in white matter architecture. Transl Psychiatry 2018;8:6. [PMID: 29317592 DOI: 10.1038/s41398-017-0022-5] [Cited by in Crossref: 31] [Cited by in F6Publishing: 31] [Article Influence: 7.8] [Reference Citation Analysis]
51 Jobin C. Precision medicine using microbiota. Science 2018;359:32-4. [DOI: 10.1126/science.aar2946] [Cited by in Crossref: 65] [Cited by in F6Publishing: 50] [Article Influence: 16.3] [Reference Citation Analysis]
52 Simon TG, Chan AT, Huttenhower C. Microbiome Biomarkers: One Step Closer in NAFLD Cirrhosis. Hepatology 2021;73:2063-6. [PMID: 33283299 DOI: 10.1002/hep.31660] [Reference Citation Analysis]
53 Chang HX, Haudenshield JS, Bowen CR, Hartman GL. Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity. Front Microbiol 2017;8:519. [PMID: 28421041 DOI: 10.3389/fmicb.2017.00519] [Cited by in Crossref: 38] [Cited by in F6Publishing: 32] [Article Influence: 7.6] [Reference Citation Analysis]
54 Cronin P, Murphy CL, Barrett M, Ghosh TS, Pellanda P, O'Connor EM, Zulquernain SA, Kileen S, McCourt M, Andrews E, O'Riordain MG, Shanahan F, O'Toole PW. Colorectal microbiota after removal of colorectal cancer. NAR Cancer 2022;4:zcac011. [PMID: 35399186 DOI: 10.1093/narcan/zcac011] [Reference Citation Analysis]
55 Ghosh TS, Das M, Jeffery IB, O'Toole PW. Adjusting for age improves identification of gut microbiome alterations in multiple diseases. Elife 2020;9:e50240. [PMID: 32159510 DOI: 10.7554/eLife.50240] [Cited by in Crossref: 30] [Cited by in F6Publishing: 13] [Article Influence: 15.0] [Reference Citation Analysis]
56 Galimberti A, Bruno A, Agostinetto G, Casiraghi M, Guzzetti L, Labra M. Fermented food products in the era of globalization: tradition meets biotechnology innovations. Curr Opin Biotechnol 2021;70:36-41. [PMID: 33232845 DOI: 10.1016/j.copbio.2020.10.006] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
57 Forbes JD, Chen CY, Knox NC, Marrie RA, El-Gabalawy H, de Kievit T, Alfa M, Bernstein CN, Van Domselaar G. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Microbiome 2018;6:221. [PMID: 30545401 DOI: 10.1186/s40168-018-0603-4] [Cited by in Crossref: 72] [Cited by in F6Publishing: 69] [Article Influence: 18.0] [Reference Citation Analysis]
58 Sinha R, Ahsan H, Blaser M, Caporaso JG, Carmical JR, Chan AT, Fodor A, Gail MH, Harris CC, Helzlsouer K, Huttenhower C, Knight R, Kong HH, Lai GY, Hutchinson DLS, Le Marchand L, Li H, Orlich MJ, Shi J, Truelove A, Verma M, Vogtmann E, White O, Willett W, Zheng W, Mahabir S, Abnet C. Next steps in studying the human microbiome and health in prospective studies, Bethesda, MD, May 16-17, 2017. Microbiome 2018;6:210. [PMID: 30477563 DOI: 10.1186/s40168-018-0596-z] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 3.8] [Reference Citation Analysis]
59 Xiao J, Chen L, Johnson S, Yu Y, Zhang X, Chen J. Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model. Front Microbiol 2018;9:1391. [PMID: 29997602 DOI: 10.3389/fmicb.2018.01391] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 4.0] [Reference Citation Analysis]
60 Vangay P, Hillmann BM, Knights D. Microbiome Learning Repo (ML Repo): A public repository of microbiome regression and classification tasks. Gigascience 2019;8:giz042. [PMID: 31042284 DOI: 10.1093/gigascience/giz042] [Cited by in Crossref: 23] [Cited by in F6Publishing: 16] [Article Influence: 7.7] [Reference Citation Analysis]
61 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]
62 Xing X, Liu JS, Zhong W. MetaGen: reference-free learning with multiple metagenomic samples. Genome Biol 2017;18:187. [PMID: 28974263 DOI: 10.1186/s13059-017-1323-y] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 1.6] [Reference Citation Analysis]
63 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]
64 Rattan P, Minacapelli CD, Rustgi V. The Microbiome and Hepatocellular Carcinoma. Liver Transpl. 2020;26:1316-1327. [PMID: 32564483 DOI: 10.1002/lt.25828] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
65 Behrouzi A, Nafari AH, Siadat SD. The significance of microbiome in personalized medicine. Clin Transl Med 2019;8:16. [PMID: 31081530 DOI: 10.1186/s40169-019-0232-y] [Cited by in Crossref: 25] [Cited by in F6Publishing: 18] [Article Influence: 8.3] [Reference Citation Analysis]
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