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Cited by 269Gut microbiota and healthGenomics and Phylogenetic StudiesMicrobial Community Ecology and Physiology

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

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Wirbel, Jakob, Zych, Konrad, Essex, Morgan, Karcher, Nicolai, Kartal, Ece, Salazar, Guillem, Bork, Peer, Sunagawa, Shinichi +1 more·2021·Genome Biology
DOI 10.1186/s13059-021-02306-1PMC PMC8008609

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Abstract

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .

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  • If spatial interdependence is ignored, localized effects may be overstated and capacity decline may be wrongly attributed only to internal stressors.

    “Ignoring spatial interdependence risks overstating localized effects or misattributing capacity decline to internal stressors alone (Wirbel et al., 2021).”

    From Climate Security, Water Governance, and Resilience How Climate-Induced Water Stress and Migration Challenge State Capacity (2026) · cites this paper as Wirbel et al., 2021

    The Results sections on confounder analysis and advanced machine-learning workflows discuss country-related confounding and dependence among repeated microbiome samples. Neither addresses spatial interdependence, localized effects, or capacity decline attributed to internal stressors (searched those terms and close paraphrases). (LLM, conf=0.77)

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  1. [3]Garrett WS The gut microbiota and colon cancer Science 2019 364 1133 1135 10.1126/science.aaw2367 31221845 · DOI
  2. [4]Gevers D, Kugathasan S, Denson LA. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe. 2014;15:382–92 Elsevier. PMC4059512 24629344
  3. [5]Franzosa EA Sirota-Madi A Avila-Pacheco J Fornelos N Haiser HJ Reinker S Gut microbiome structure and metabolic activity in inflammatory bowel disease Nat Microbiol 2019 4 293 305 10.1038/s41564-018-0306-4 30531976 PMC6342642 · DOI · Paper page
  4. [6]Zhang X Zhang D Jia H Feng Q Wang D Liang D The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment Nat Med 2015 21 895 905 10.1038/nm.3914 26214836 · DOI · Paper page
  5. [7]Wen C Zheng Z Shao T Liu L Xie Z Le Chatelier E Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis Genome Biol 2017 18 142 10.1186/s13059-017-1271-6 28750650 PMC5530561 · DOI
  6. [9]Tilg H Cani PD Mayer EA Gut microbiome and liver diseases Gut 2016 65 2035 2044 10.1136/gutjnl-2016-312729 27802157 · DOI · Paper page
  7. [10]Zitvogel L Ma Y Raoult D Kroemer G Gajewski TF The microbiome in cancer immunotherapy: diagnostic tools and therapeutic strategies Science 2018 359 1366 1370 10.1126/science.aar6918 29567708 · DOI · Paper page
  8. [11]Forslund K Hildebrand F Nielsen T Falony G Le Chatelier E Sunagawa S Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota Nature. 2015 528 262 266 10.1038/nature15766 26633628 PMC4681099 · DOI · Paper page
  9. [12]Voigt AY Costea PI Kultima JR Li SS Zeller G Sunagawa S Temporal and technical variability of human gut metagenomes Genome Biol 2015 16 73 10.1186/s13059-015-0639-8 25888008 PMC4416267 · DOI
  10. [13]McLaren MR Willis AD Callahan BJ Consistent and correctable bias in metagenomic sequencing experiments Elife. 2019 8 e46923 10.7554/eLife.46923 31502536 PMC6739870 · DOI
  11. [14]Segata N Izard J Waldron L Gevers D Miropolsky L Garrett WS Metagenomic biomarker discovery and explanation Genome Biol 2011 12 R60 10.1186/gb-2011-12-6-r60 21702898 PMC3218848 · DOI · Paper page
  12. [15]Paulson JN Stine OC Bravo HC Pop M Differential abundance analysis for microbial marker-gene surveys Nat Methods 2013 10 1200 1202 10.1038/nmeth.2658 24076764 PMC4010126 · DOI
  13. [17]Knights D Parfrey LW Zaneveld J Lozupone C Knight R Human-associated microbial signatures: examining their predictive value Cell Host Microbe 2011 10 292 296 10.1016/j.chom.2011.09.003 22018228 PMC3879110 · DOI
  14. [18]Knights D Costello EK Knight R Supervised classification of human microbiota FEMS Microbiol Rev 2011 35 343 359 10.1111/j.1574-6976.2010.00251.x 21039646 · DOI
  15. [20]Duvallet C Gibbons SM Gurry T Irizarry RA Alm EJ Meta-analysis of gut microbiome studies identifies disease-specific and shared responses Nat Commun 2017 8 1784 10.1038/s41467-017-01973-8 29209090 PMC5716994 · DOI
  16. [21]Wang J Kurilshikov A Radjabzadeh D Turpin W Croitoru K Bonder MJ Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative Microbiome 2018 6 101 10.1186/s40168-018-0479-3 29880062 PMC5992867 · DOI
  17. [22]Bang S Yoo D Kim S-J 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 10.1038/s41598-019-46249-x 31308384 PMC6629854 · DOI
  18. [23]Zhou Y-H Gallins P A review and tutorial of machine learning methods for microbiome host trait prediction Front Genet 2019 10 579 10.3389/fgene.2019.00579 31293616 PMC6603228 · DOI
  19. [24]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 10.1371/journal.pcbi.1007895 32392251 PMC7241849 · DOI
  20. [25]Scher JU Sczesnak A Longman RS Segata N Ubeda C Bielski C Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis Elife. 2013 2 e01202 10.7554/eLife.01202 24192039 PMC3816614 · DOI · Paper page
  21. [26]Alkanani AK Hara N Gottlieb PA Ir D Robertson CE Wagner BD Alterations in intestinal microbiota correlate with susceptibility to type 1 diabetes Diabetes 2015 64 3510 3520 10.2337/db14-1847 26068542 PMC4587635 · DOI
  22. [27]Nielsen HB Almeida M Juncker AS Rasmussen S Li J Sunagawa S Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes Nat Biotechnol 2014 32 822 828 10.1038/nbt.2939 24997787 · DOI · Paper page
  23. [28]Lewis JD Chen EZ Baldassano RN Otley AR Griffiths AM Lee D Inflammation, antibiotics, and diet as environmental stressors of the gut microbiome in pediatric Crohn’s disease Cell Host Microbe 2015 18 489 500 10.1016/j.chom.2015.09.008 26468751 PMC4633303 · DOI · Paper page
  24. [29]He Q Gao Y Jie Z Yu X Laursen JM Xiao L Two distinct metacommunities characterize the gut microbiota in Crohn’s disease patients Gigascience 2017 6 1 11 10.1093/gigascience/gix050 28655159 PMC5624284 · DOI
  25. [31]Willing BP Dicksved J Halfvarson J Andersson AF Lucio M Zheng Z A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes Gastroenterology 2010 139 1844 54.e1 10.1053/j.gastro.2010.08.049 20816835 · DOI · Paper page
  26. [32]Morgan XC Tickle TL Sokol H Gevers D Devaney KL Ward DV Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment Genome Biol 2012 13 R79 10.1186/gb-2012-13-9-r79 23013615 PMC3506950 · DOI · Paper page
  27. [33]Papa E Docktor M Smillie C Weber S Preheim SP Gevers D Non-invasive mapping of the gastrointestinal microbiota identifies children with inflammatory bowel disease PLoS One 2012 7 e39242 10.1371/journal.pone.0039242 22768065 PMC3387146 · DOI
  28. [35]Yu J Feng Q Wong SH Zhang D Liang QY Qin Y Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer Gut 2017 66 70 78 10.1136/gutjnl-2015-309800 26408641 · DOI
  29. [38]Thomas AM Manghi P Asnicar F Pasolli E Armanini F Zolfo M Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation Nat Med 2019 25 667 678 10.1038/s41591-019-0405-7 30936548 PMC9533319 · DOI · Paper page
  30. [39]Yachida S Mizutani S Shiroma H Shiba S Nakajima T Sakamoto T Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer Nat Med 2019 25 968 976 10.1038/s41591-019-0458-7 31171880 · DOI · Paper page
  31. [41]Chen W Liu F Ling Z Tong X Xiang C Human intestinal lumen and mucosa-associated microbiota in patients with colorectal cancer PLoS One 2012 7 e39743 10.1371/journal.pone.0039743 22761885 PMC3386193 · DOI
  32. [42]Baxter NT Ruffin MT 4th Rogers MAM Schloss PD Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions Genome Med 2016 8 37 10.1186/s13073-016-0290-3 27056827 PMC4823848 · DOI · Paper page
  33. [44]Li J Zhao F Wang Y Chen J Tao J Tian G Gut microbiota dysbiosis contributes to the development of hypertension Microbiome. 2017 5 14 10.1186/s40168-016-0222-x 28143587 PMC5286796 · DOI · Paper page
  34. [45]Schubert AM Rogers MAM Ring C Mogle J Petrosino JP Young VB Microbiome data distinguish patients with Clostridium difficile infection and non-C. difficile-associated diarrhea from healthy controls MBio 2014 5 e01021 e01014 10.1128/mBio.01021-14 24803517 PMC4010826 · DOI
  35. [46]Vincent C Stephens DA Loo VG Edens TJ Behr MA Dewar K Reductions in intestinal Clostridiales precede the development of nosocomial Clostridium difficile infection Microbiome. 2013 1 18 10.1186/2049-2618-1-18 24450844 PMC3971611 · DOI
  36. [47]Singh P Teal TK Marsh TL Tiedje JM Mosci R Jernigan K Intestinal microbial communities associated with acute enteric infections and disease recovery Microbiome. 2015 3 45 10.1186/s40168-015-0109-2 26395244 PMC4579588 · DOI
  37. [48]Lozupone CA Li M Campbell TB Flores SC Linderman D Gebert MJ Alterations in the gut microbiota associated with HIV-1 infection Cell Host Microbe 2013 14 329 339 10.1016/j.chom.2013.08.006 24034618 PMC3864811 · DOI
  38. [49]Dinh DM Volpe GE Duffalo C Bhalchandra S Tai AK Kane AV Intestinal microbiota, microbial translocation, and systemic inflammation in chronic HIV infection J Infect Dis 2015 211 19 27 10.1093/infdis/jiu409 25057045 PMC4326316 · DOI
  39. [50]Noguera-Julian M Rocafort M Guillén Y Rivera J Casadellà M Nowak P Gut microbiota linked to sexual preference and HIV infection EBioMedicine. 2016 5 135 146 10.1016/j.ebiom.2016.01.032 27077120 PMC4816837 · DOI
  40. [51]Qin N Yang F Li A Prifti E Chen Y Shao L Alterations of the human gut microbiome in liver cirrhosis Nature. 2014 513 59 64 10.1038/nature13568 25079328 · DOI · Paper page
  41. [52]Zhang Z Zhai H Geng J Yu R Ren H Fan H Large-scale survey of gut microbiota associated with MHE via 16S rRNA-based pyrosequencing Am J Gastroenterol 2013 108 1601 1611 10.1038/ajg.2013.221 23877352 · DOI
  42. [53]Loomba R Seguritan V Li W Long T Klitgord N Bhatt A Gut microbiome-based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease Cell Metab 2017 25 1054 62.e5 10.1016/j.cmet.2017.04.001 28467925 PMC5502730 · DOI · Paper page
  43. [54]Hoyles L Fernández-Real J-M Federici M Serino M Abbott J Charpentier J Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women Nat Med 2018 24 1070 1080 10.1038/s41591-018-0061-3 29942096 PMC6140997 · DOI · Paper page
  44. [55]Wong VW-S Tse C-H Lam TT-Y Wong GL-H Chim AM-L Chu WC-W Molecular characterization of the fecal microbiota in patients with nonalcoholic steatohepatitis--a longitudinal study PLoS One 2013 8 e62885 10.1371/journal.pone.0062885 23638162 PMC3636208 · DOI
  45. [57]Bedarf JR Hildebrand F Coelho LP Sunagawa S Bahram M Goeser F Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients Genome Med 2017 9 39 10.1186/s13073-017-0428-y 28449715 PMC5408370 · DOI · Paper page
  46. [59]Kang D-W Park JG Ilhan ZE Wallstrom G Labaer J Adams JB Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children PLoS One 2013 8 e68322 10.1371/journal.pone.0068322 23844187 PMC3700858 · DOI · Paper page
  47. [63]Turnbaugh PJ Hamady M Yatsunenko T Cantarel BL Duncan A Ley RE A core gut microbiome in obese and lean twins Nature. 2009 457 480 484 10.1038/nature07540 19043404 PMC2677729 · DOI · Paper page
  48. [64]Zupancic ML Cantarel BL Liu Z Drabek EF Ryan KA Cirimotich S Analysis of the gut microbiota in the old order Amish and its relation to the metabolic syndrome PLoS One 2012 7 e43052 10.1371/journal.pone.0043052 22905200 PMC3419686 · DOI
  49. [65]Ross MC Muzny DM McCormick JB Gibbs RA Fisher-Hoch SP Petrosino JF 16S gut community of the Cameron County Hispanic Cohort Microbiome. 2015 3 7 10.1186/s40168-015-0072-y 25763184 PMC4355967 · DOI
  50. [66]Kushugulova A Forslund SK Costea PI Kozhakhmetov S Khassenbekova Z Urazova M Metagenomic analysis of gut microbial communities from a Central Asian population BMJ Open 2018 8 e021682 10.1136/bmjopen-2018-021682 30056386 PMC6067398 · DOI
  51. [69]McMurdie PJ Holmes S phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data PLoS One 2013 8 e61217 10.1371/journal.pone.0061217 23630581 PMC3632530 · DOI
  52. [71]Frank DN St Amand AL Feldman RA Boedeker EC Harpaz N Pace NR Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases Proc Natl Acad Sci U S A 2007 104 13780 13785 10.1073/pnas.0706625104 17699621 PMC1959459 · DOI · Paper page
  53. [72]Pasolli E Schiffer L Manghi P Renson A Obenchain V Truong DT Accessible, curated metagenomic data through ExperimentHub Nat Methods 2017 14 1023 1024 10.1038/nmeth.4468 29088129 PMC5862039 · DOI · Paper page
  54. [75]Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 2005;67(2):301–20 Wiley Online Library.
  55. [76]Ho TK Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition 1995 278 282
  56. [77]Deloris Alexander A Orcutt RP Henry JC Baker J Bissahoyo AC Threadgill DW Quantitative PCR assays for mouse enteric flora reveal strain-dependent differences in composition that are influenced by the microenvironment Mamm Genome 2006 17 1093 1104 10.1007/s00335-006-0063-1 17091319 · DOI
  57. [79]Jackson MA Goodrich JK Maxan M-E Freedberg DE Abrams JA Poole AC Proton pump inhibitors alter the composition of the gut microbiota Gut 2016 65 749 756 10.1136/gutjnl-2015-310861 26719299 PMC4853574 · DOI · Paper page
  58. [81]Smialowski P Frishman D Kramer S Pitfalls of supervised feature selection Bioinformatics. 2010 26 440 443 10.1093/bioinformatics/btp621 19880370 PMC2815655 · DOI
  59. [82]Roberts DR Bahn V Ciuti S Boyce MS Elith J Guillera-Arroita G Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure Ecography 2017 40 913 929 10.1111/ecog.02881 · DOI · Paper page
  60. [83]Wang Q Garrity GM Tiedje JM Cole JR Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy Appl Environ Microbiol Am Soc Microbiol 2007 73 5261 5267 10.1128/AEM.00062-07 PMC1950982 17586664 · DOI
  61. [86]Franzosa EA McIver LJ Rahnavard G Thompson LR Schirmer M Weingart G Species-level functional profiling of metagenomes and metatranscriptomes Nat Methods 2018 15 962 968 10.1038/s41592-018-0176-y 30377376 PMC6235447 · DOI · Paper page
  62. [87]Huerta-Cepas J Szklarczyk D Forslund K Cook H Heller D Walter MC eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences Nucleic Acids Res 2016 44 D286 D293 10.1093/nar/gkv1248 26582926 PMC4702882 · DOI
  63. [88]Lozupone CA Stombaugh J Gonzalez A Ackermann G Wendel D Vázquez-Baeza Y Meta-analyses of studies of the human microbiota Genome Res 2013 23 1704 1714 10.1101/gr.151803.112 23861384 PMC3787266 · DOI · Paper page
  64. [90]Costea PI Zeller G Sunagawa S Pelletier E Alberti A Levenez F Towards standards for human fecal sample processing in metagenomic studies Nat Biotechnol 2017 35 1069 1076 10.1038/nbt.3960 28967887 · DOI · Paper page
  65. [92]Olesen SW Alm EJ Dysbiosis is not an answer Nat Microbiol 2016 1 16228 10.1038/nmicrobiol.2016.228 27886190 · DOI · Paper page
  66. [93]Zeevi D Korem T Zmora N Israeli D Rothschild D Weinberger A Personalized nutrition by prediction of glycemic responses Cell. 2015 163 1079 1094 10.1016/j.cell.2015.11.001 26590418 · DOI · Paper page
  67. [94]Schirmer M Smeekens SP Vlamakis H Jaeger M Oosting M Franzosa EA Linking the human gut microbiome to inflammatory cytokine production capacity Cell 2016 167 1897 10.1016/j.cell.2016.11.046 27984736 · DOI
  68. [95]Xie H Guo R Zhong H Feng Q Lan Z Qin B Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome Cell Syst 2016 3 572 84.e3 10.1016/j.cels.2016.10.004 27818083 PMC6309625 · DOI · Paper page
  69. [96]Bernstein CN Blanchard JF Kliewer E Wajda A Cancer risk in patients with inflammatory bowel disease: a population-based study Cancer 2001 91 854 862 10.1002/1097-0142(20010215)91:4<854::AID-CNCR1073>3.0.CO;2-Z 11241255 · DOI
  70. [97]Manichanh C Rigottier-Gois L Bonnaud E Gloux K Pelletier E Frangeul L Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach Gut 2006 55 205 211 10.1136/gut.2005.073817 16188921 PMC1856500 · DOI · Paper page
  71. [98]Callahan BJ McMurdie PJ Rosen MJ Han AW Johnson AJA Holmes SP DADA2: high-resolution sample inference from Illumina amplicon data Nat Methods 2016 13 581 583 10.1038/nmeth.3869 27214047 PMC4927377 · DOI · Paper page
  72. [99]Cani PD Gut microbiota - at the intersection of everything? Nat Rev Gastroenterol Hepatol 2017 14 321 322 10.1038/nrgastro.2017.54 28442782 · DOI
  73. [100]Yilmaz P Kottmann R Field D Knight R Cole JR Amaral-Zettler L Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications Nat Biotechnol 2011 29 415 420 10.1038/nbt.1823 21552244 PMC3367316 · DOI
  74. [102]Horvath A Rainer F Bashir M Leber B Schmerboeck B Klymiuk I Biomarkers for oralization during long-term proton pump inhibitor therapy predict survival in cirrhosis Sci Rep 2019 9 12000 10.1038/s41598-019-48352-5 31427714 PMC6700098 · DOI · Paper page
  75. [103]Rubinstein MR Wang X Liu W Hao Y Cai G Han YW Fusobacterium nucleatum promotes colorectal carcinogenesis by modulating E-cadherin/β-catenin signaling via its FadA adhesin Cell Host Microbe 2013 14 195 206 10.1016/j.chom.2013.07.012 23954158 PMC3770529 · DOI · Paper page
  76. [104]Ohkusa T Okayasu I Ogihara T Morita K Ogawa M Sato N Induction of experimental ulcerative colitis by Fusobacterium varium isolated from colonic mucosa of patients with ulcerative colitis Gut 2003 52 79 83 10.1136/gut.52.1.79 12477765 PMC1773498 · DOI
  77. [105]Salazar G Paoli L Alberti A Huerta-Cepas J Ruscheweyh H-J Cuenca M Gene expression changes and community turnover differentially shape the global ocean metatranscriptome Cell 2019 179 1068 83.e21 10.1016/j.cell.2019.10.014 31730850 PMC6912165 · DOI
  78. [106]Coelho LP Alves R Monteiro P Huerta-Cepas J Freitas AT Bork P NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language Microbiome 2019 7 84 10.1186/s40168-019-0684-8 31159881 PMC6547473 · DOI
  79. [107]Kultima JR Coelho LP Forslund K Huerta-Cepas J Li SS Driessen M MOCAT2: a metagenomic assembly, annotation and profiling framework Bioinformatics 2016 32 2520 2523 10.1093/bioinformatics/btw183 27153620 PMC4978931 · DOI
  80. [108]Li J Jia H Cai X Zhong H Feng Q Sunagawa S An integrated catalog of reference genes in the human gut microbiome Nat Biotechnol 2014 32 834 841 10.1038/nbt.2942 24997786 · DOI · Paper page
  81. [109]Fox J, Weisberg S. An R companion to applied regression. Thousand Oaks: SAGE Publications; 2018.
  82. [110]Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, et al. Data for “Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox”. Zenodo. Available from: 10.5281/zenodo.4454489 (2021). PMC8008609 33785070 · DOI
  83. [111]Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, et al. Analysis code for the SIAMCAT manuscript. GitHub. Available from: https://github.com/zellerlab/siamcat_paper (2021). PMC8008609 33785070
  84. [112]Wirbel J, Zych K, Essex M, Karcher N, Zeller G. SIAMCAT source code. GitHub. Available from: https://github.com/zellerlab/siamcat (2021). PMC8008609 33785070
  85. [113]Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, et al. Code for “Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox”. Zenodo. Available from: 10.5281/zenodo.4457522 (2021). PMC8008609 33785070 · DOI

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Authors
Wirbel, J., Zych, K., Essex, M., Karcher, N., Kartal, E., Salazar, G., Bork, P., Sunagawa, S., & Zeller, G.
Affiliations
  • European Molecular Biology Laboratory (DE)
  • Clinical Microbiomics (Denmark) (DK)
  • Max Delbrück Center (DE)
  • University of Trento (IT)
  • SIB Swiss Institute of Bioinformatics (CH)
Corresponding author
Zeller, Georg
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2021-03-30
Volume
22
Issue
1
Pages
93-93
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-021-02306-1
PMC
PMC8008609
OpenAlex
W3143324940
Cited by
269
References
25
Citations by year
2026: 45 · 2025: 44 · 2024: 61 · 2023: 46 · 2022: 56 · 2021: 15 · 2020: 1 · 2019: 1
FWCI
14.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • European Molecular Biology Laboratory
  • Eidgenössische Technische Hochschule Zürich (PHRT 521)
  • Bundesministerium für Bildung und Forschung (031A537B, 031L0181A)
  • Deutsche Forschungsgemeinschaft (395357507)
  • Helmut Horten Stiftung
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
Keywords
human microbiome, comparative metagenomics, machine learning, training set augmentation, disease-specific biomarkers, shared biomarkers, fecal microbiota, overfitting
MeSH terms
Gastrointestinal Microbiome, Machine Learning, Crohn Disease, Humans, ROC Curve, Software, Meta-Analysis as Topic, Models, Statistical, Confounding Factors, Epidemiologic, Computational Biology, Databases, Genetic, Metagenome, Metagenomics, Workflow, Microbiota

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