Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Gut microbiome, big data and machine learning to promote precision medicine for cancer

Abstract

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The ‘omics’ technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Representation of a machine learning workflow.
Fig. 2: From big data to precision medicine: moving through the data science.

Similar content being viewed by others

References

  1. Marchesi, J. R. et al. The gut microbiota and host health: a new clinical frontier. Gut 65, 330–339 (2016).

    PubMed  Google Scholar 

  2. Yue, B. et al. Inflammatory bowel disease: a potential result from the collusion between gut microbiota and mucosal immune system. Microorganisms 7, E440 (2019).

    PubMed  Google Scholar 

  3. Zhang, Z. et al. Impact of fecal microbiota transplantation on obesity and metabolic syndrome — a systematic review. Nutrients 11, E2291 (2019).

    PubMed  Google Scholar 

  4. Thaiss, C. A. et al. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature 540, 544–551 (2016).

    CAS  PubMed  Google Scholar 

  5. Mullish, B. H. & Williams, H. R. Clostridium difficile infection and antibiotic-associated diarrhoea. Clin. Med. 18, 237–241 (2018).

    Google Scholar 

  6. van der Giessen, J. et al. Modulation of cytokine patterns and microbiome during pregnancy in IBD. Gut 69, 473–486 (2020).

    PubMed  Google Scholar 

  7. Konstantinov, S. R., van der Woude, C. J. & Peppelenbosch, M. P. Do pregnancy-related changes in the microbiome stimulate innate immunity? Trends Mol. Med. 19, 454–459 (2013).

    CAS  PubMed  Google Scholar 

  8. Maguire, M. & Maguire, G. Gut dysbiosis, leaky gut, and intestinal epithelial proliferation in neurological disorders: towards the development of a new therapeutic using amino acids, prebiotics, probiotics, and postbiotics. Rev. Neurosci. 30, 179–201 (2019).

    PubMed  Google Scholar 

  9. Tang, W. H. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Vivarelli, S. et al. Gut microbiota and cancer: from pathogenesis to therapy. Cancers 11, 38 (2019).

    CAS  PubMed Central  Google Scholar 

  11. Bi, J. H. et al. ClickGene: an open cloud-based platform for big pan-cancer data genome-wide association study, visualization and exploration. BioData Min. 12, 12 (2019).

    PubMed  PubMed Central  Google Scholar 

  12. Rodriguez-Martin, B. et al. Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition. Nat. Genet. 52, 306–319 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang, J. et al. The International Cancer Genome Consortium data portal. Nat. Biotechnol. 37, 367–369 (2019).

    CAS  PubMed  Google Scholar 

  14. Brown, J. A., Ni Chonghaile, T., Matchett, K. B., Lynam-Lennon, N. & Kiely, P. A. Big data-led cancer research, application, and insights. Cancer Res. 76, 6167–6170 (2016).

    CAS  PubMed  Google Scholar 

  15. Evans, B. J. & Krumholz, H. M. People-powered data collaboratives: fueling data science with the health-related experiences of individuals. J. Am. Med. Inform. Assoc. 26, 159–161 (2019).

    PubMed  Google Scholar 

  16. Provost, F. & Fawcett, T. Data science and its relationship to big data and data-driven decision making. Big Data 1, 51–59 (2013).

    PubMed  Google Scholar 

  17. Sanchez-Pinto, L. N., Luo, Y. & Churpek, M. M. Big data and data science in critical care. Chest 154, 1239–1248 (2018).

    PubMed  PubMed Central  Google Scholar 

  18. Gruson, D., Helleputte, T., Rousseau, P. & Gruson, D. Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the value of positive regulation. Clin. Biochem. 69, 1–7 (2019).

    PubMed  Google Scholar 

  19. Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).

    PubMed  Google Scholar 

  20. Sender, R., Fuchs, S. & Milo, R. Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell 164, 337–340 (2016).

    CAS  PubMed  Google Scholar 

  21. Lozupone, C. A. et al. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Conlon, M. A. & Bird, A. R. The impact of diet and lifestyle on gut microbiota and human health. Nutrients 7, 17–44 (2014).

    PubMed  PubMed Central  Google Scholar 

  23. Imhann, F. et al. The influence of proton pump inhibitors and other commonly used medication on the gut microbiota. Gut Microbes 8, 351–358 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Thomas, S. et al. The host microbiome regulates and maintains human health: a primer and perspective for non-microbiologists. Cancer Res. 77, 1783–1812 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Fessler, J., Matson, V. & Gajewski, T. F. Exploring the emerging role of the microbiome in cancer immunotherapy. J. Immunother. Cancer 7, 108 (2019).

    PubMed  PubMed Central  Google Scholar 

  26. Scott, A. J. et al. International Cancer Microbiome Consortium consensus statement on the role of the human microbiome in carcinogenesis. Gut 68, 1624–1632 (2019).

    CAS  PubMed  Google Scholar 

  27. Lazar, V. et al. Aspects of gut microbiota and immune system interactions in infectious diseases, immunopathology, and cancer. Front. Immunol. 9, 1830 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. Pagliari, D. et al. Gut microbiota–immune system crosstalk and pancreatic disorders. Mediators Inflamm. 2018, 7946431 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Bingula, R. et al. Desired turbulence? Gut–lung axis, immunity, and lung cancer. J. Oncol. 2017, 5035371 (2017).

    PubMed  PubMed Central  Google Scholar 

  30. Gopalakrishnan, V. et al. The influence of the gut microbiome on cancer, immunity, and cancer immunotherapy. Cancer Cell 33, 570–580 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Rugge, M. et al. Gastric cancer as preventable disease. Clin. Gastroenterol. Hepatol. 15, 1833–1843 (2017).

    PubMed  Google Scholar 

  32. Parsonnet, J. et al. Helicobacter pylori infection and the risk of gastric carcinoma. N. Engl. J. Med. 325, 1127–1131 (1991).

    CAS  PubMed  Google Scholar 

  33. Garrett, W. S. Cancer and the microbiota. Science 348, 80–86 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Wu, S. et al. A human colonic commensal promotes colon tumorigenesis via activation of T helper type 17 T cell responses. Nat. Med. 15, 1016–1022 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Raza, M. H. et al. Microbiota in cancer development and treatment. J. Cancer Res. Clin. Oncol. 145, 49–63 (2019).

    CAS  PubMed  Google Scholar 

  36. Pushalkar, S. et al. The pancreatic cancer microbiome promotes oncogenesis by induction of innate and adaptive immune suppression. Cancer Discov. 8, 403–416 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207–215 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Rubinstein, M. R. et al. Fusobacterium nucleatum promotes colorectal carcinogenesis by modulating E-cadherin/beta-catenin signaling via its FadA adhesin. Cell Host Microbe 14, 195–206 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Yoshimoto, S. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499, 97–101 (2013).

    CAS  PubMed  Google Scholar 

  40. Raskov, H., Burcharth, J. & Pommergaard, H. C. Linking gut microbiota to colorectal cancer. J. Cancer 8, 3378–3395 (2017).

    PubMed  PubMed Central  Google Scholar 

  41. Li, S., Peppelenboscha, M. P. & Smits, R. Bacterial biofilms as a potential contributor to mucinous colorectal cancer formation. Biochim. Biophys. Acta Rev. Cancer 1872, 74–79 (2019).

    CAS  PubMed  Google Scholar 

  42. Belkaid, Y. & Hand, T. W. Role of the microbiota in immunity and inflammation. Cell 157, 121–141 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 66, 70–78 (2017).

    CAS  PubMed  Google Scholar 

  44. Zackular, J. P. et al. The gut microbiome modulates colon tumorigenesis. mBio 4, e00692-13 (2013).

    PubMed  PubMed Central  Google Scholar 

  45. Wirbel, J. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Sobhani, I. et al. Microbial dysbiosis in colorectal cancer (CRC) patients. PLoS One 6, e16393 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Ren, Z. et al. Gut microbial profile analysis by MiSeq sequencing of pancreatic carcinoma patients in China. Oncotarget 8, 95176–95191 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. Ren, Z. et al. Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma. Gut 58, 1014–1023 (2019).

    Google Scholar 

  49. Pouncey, A. L. et al. Gut microbiota, chemotherapy and the host: the influence of the gut microbiota on cancer treatment. Ecancermedicalscience 12, 868 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. Alexander, J. L. et al. Gut microbiota modulation of chemotherapy efficacy and toxicity. Nat. Rev. Gastroenterol. Hepatol. 14, 356–365 (2017).

    CAS  PubMed  Google Scholar 

  51. Touchefeu, Y. et al. Systematic review: the role of the gut microbiota in chemotherapy- or radiation-induced gastrointestinal mucositis — current evidence and potential clinical applications. Aliment. Pharmacol. Ther. 40, 409–421 (2014).

    CAS  PubMed  Google Scholar 

  52. Mathijssen, R. H. et al. Clinical pharmacokinetics and metabolism of irinotecan (CPT-11). Clin. Cancer Res. 7, 2182–2194 (2001).

    CAS  PubMed  Google Scholar 

  53. Ma, M. K. & McLeod, H. L. Lessons learned from the irinotecan metabolic pathway. Curr. Med. Chem. 10, 41–49 (2003).

    CAS  PubMed  Google Scholar 

  54. Wallace, B. D. et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330, 831–835 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Kodawara, T. et al. The inhibitory effect of ciprofloxacin on the beta-glucuronidase-mediated deconjugation of the irinotecan metabolite SN-38-G. Basic Clin. Pharmacol. Toxicol. 118, 333–337 (2016).

    CAS  PubMed  Google Scholar 

  56. Frank, M. et al. TLR signaling modulates side effects of anticancer therapy in the small intestine. J. Immunol. 194, 1983–1995 (2015).

    CAS  PubMed  Google Scholar 

  57. Hooper, L. V. & Macpherson, A. J. Immune adaptations that maintain homeostasis with the intestinal microbiota. Nat. Rev. Immunol. 10, 159–169 (2010).

    CAS  PubMed  Google Scholar 

  58. Quince, C. et al. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).

    CAS  PubMed  Google Scholar 

  59. Vetizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 1079–1084 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Frankel, A. E. et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 19, 848–855 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).

    CAS  PubMed  Google Scholar 

  62. Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).

    CAS  PubMed  Google Scholar 

  63. Sivan, A. et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350, 1084–1089 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Gerassy-Vainberg, S. et al. Radiation induces proinflammatory dysbiosis: transmission of inflammatory susceptibility by host cytokine induction. Gut 67, 97–107 (2018).

    CAS  PubMed  Google Scholar 

  65. Kumagai, T., Rahman, F. & Smith, A. M. The microbiome and radiation induced-bowel injury: evidence for potential mechanistic role in disease pathogenesis. Nutrients 10, E1405 (2018).

    PubMed  Google Scholar 

  66. Cui, M. et al. Faecal microbiota transplantation protects against radiation-induced toxicity. EMBO Mol. Med. 9, 448–461 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Manichanh, C. et al. The gut microbiota predispose to the pathophysiology of acute postradiotherapy diarrhea. Am. J. Gastroenterol. 103, 1754–1761 (2008).

    CAS  PubMed  Google Scholar 

  68. Nam, Y. D. et al. Impact of pelvic radiotherapy on gut microbiota of gynecological cancer patients revealed by massive pyrosequencing. PLoS One 8, e82659 (2013).

    PubMed  PubMed Central  Google Scholar 

  69. Wang, A. et al. Gut microbial dysbiosis may predict diarrhea and fatigue in patients undergoing pelvic cancer radiotherapy: a pilot study. PLoS One 10, e0126312 (2015).

    PubMed  PubMed Central  Google Scholar 

  70. Reis Ferreira, M. et al. Microbiota- and radiotherapy-induced gastrointestinal side-effects (MARS) study: a large pilot study of the microbiome in acute and late-radiation enteropathy. Clin. Cancer Res. 25, 6487–6500 (2019).

    PubMed  Google Scholar 

  71. Lam, S. Y., Peppelenbosch, M. P. & Fuhler, G. M. Prediction and treatment of radiation enteropathy: can intestinal bugs lead the way? Clin. Cancer Res. 25, 6280–6282 (2019).

    CAS  PubMed  Google Scholar 

  72. Roy, S. & Trinchieri, G. Microbiota: a key orchestrator of cancer therapy. Nat. Rev. Cancer. 17, 271–285 (2017).

    CAS  PubMed  Google Scholar 

  73. Lehouritis, P. et al. Local bacteria affect the efficacy of chemotherapeutic drugs. Sci. Rep. 5, 14554 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Viaud, S. et al. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342, 971–976 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Ghiringhelli, F. et al. Activation of the NLRP3 inflammasome in dendritic cells induces IL-1beta-dependent adaptive immunity against tumors. Nat. Med. 15, 1170–1178 (2009).

    CAS  PubMed  Google Scholar 

  76. Ozben, T. Oxidative stress and apoptosis: impact on cancer therapy. J. Pharm. Sci. 96, 2181–2196 (2007).

    CAS  PubMed  Google Scholar 

  77. Iida, N. et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342, 967–970 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Daillere, R. et al. Enterococcus hirae and Barnesiella intestinihominis facilitate cyclophosphamide-induced therapeutic immunomodulatory effects. Immunity 45, 931–943 (2016).

    CAS  PubMed  Google Scholar 

  79. Fyza, Y., Gills, J. & Sears, C. L. Impact of the microbiome on checkpoint inhibitor treatment in patients with non-small cell lung cancer and melanoma. EBioMedicine 48, 642–647 (2019).

    Google Scholar 

  80. Seidel, J. A., Otsuka, A. & Kabashima, K. Anti-PD-1 and anti-CTLA-4 therapies in cancer: mechanisms of action, efficacy, and limitations. Front. Oncol. 8, 86 (2018).

    PubMed  PubMed Central  Google Scholar 

  81. Darvin, P., Toor, S. M., Sasidharan Nair, V. & Elkord, E. Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp. Mol. Med. 50, 1–11 (2018).

    PubMed  Google Scholar 

  82. Yang, B. et al. Progresses and perspectives of anti-PD-1/PD-L1 antibody therapy in head and neck cancers. Front. Oncol. 8, 563 (2018).

    PubMed  PubMed Central  Google Scholar 

  83. Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Peled, J. U. et al. Microbiota predictor of mortality in allogeneic hematopoietic-cell transplantation. N. Engl. J. Med. 382, 822–834 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Schwabe, R. F. & Jobin, C. The microbiome and cancer. Nat. Rev. Cancer 13, 800–812 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Elinav, E. et al. The cancer microbiome. Nat. Rev. Cancer. 19, 371–376 (2018).

    Google Scholar 

  87. de Martel, C. et al. Global burden of cancers attributable to infections in 2008: a review and synthetic analysis. Lancet Oncol. 13, 607–615 (2012).

    PubMed  Google Scholar 

  88. Fais, T. et al. Targeting colorectal cancer-associated bacteria: a new area of research for personalized treatments. Gut Microbes 7, 329–333 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Shah, M. S. et al. Leveraging sequence-based faecal microbial community survey data to identify a composite biomarker for colorectal cancer. Gut 67, 882–891 (2018).

    CAS  PubMed  Google Scholar 

  90. Armour, C. R., Nayfach, S., Pollard, K. S. & Sharpton, T. J. A metagenomic meta-analysis reveals functional signatures of health and disease in the human gut microbiome. mSystems 4, e00332-18 (2019).

    PubMed  PubMed Central  Google Scholar 

  91. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

    PubMed  PubMed Central  Google Scholar 

  92. Bhatt, A. S. et al. Sequence-based discovery of Bradyrhizobium enterica in cord colitis syndrome. N. Engl. J. Med. 369, 517–528 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Kostic, A. D. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292–298 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Drewes, J. L. et al. High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia. NPJ Biofilms Microbiomes 3, 34 (2017).

    PubMed  PubMed Central  Google Scholar 

  95. Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019).

    CAS  PubMed  Google Scholar 

  96. Esteban-Gil, A. et al. ColPortal, an integrative multiomic platform for analysing epigenetic interactions in colorectal cancer. Sci. Data 6, 255 (2019).

    PubMed  PubMed Central  Google Scholar 

  97. Derosa, L. et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann. Oncol. 29, 1437–1444 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Li, Y., Wu, F. X. & Ngom, A. A review on machine learning principles for multi-view biological data integration. Brief. Bioinform. 19, 325–340 (2018).

    PubMed  Google Scholar 

  99. Alanazi, H. O., Abdullah, A. H. & Qureshi, K. N. A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J. Med. Syst. 41, 69 (2017).

    PubMed  Google Scholar 

  100. Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C. & Collins, J. J. Next-generation machine learning for biological networks. Cell 173, 1581–1592 (2018).

    CAS  PubMed  Google Scholar 

  101. Zhang, Y. et al. Machine learning performance in a microbial molecular autopsy context: a cross-sectional postmortem human population study. PLoS One 14, e0213829 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Ruffle, J. K., Farmer, A. D. & Aziz, Q. Artificial intelligence-assisted gastroenterology — promises and pitfalls. Am. J. Gastroenterol. 114, 422–428 (2019).

    PubMed  Google Scholar 

  103. Saito, H. et al. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest. Endosc. 92, 144–151 (2020).

    PubMed  Google Scholar 

  104. Lui, T. K., Guo, C. G. & Leung, W. K. Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis. Gastrointest. Endosc. 92, 11–22 (2020).

    PubMed  Google Scholar 

  105. Seyed Tabib, N. S. et al. Big data in IBD: big progress for clinical practice. Gut https://doi.org/10.1136/gutjnl-2019-320065 (2020).

  106. Olivera, P., Danese, S., Jay, N., Natoli, G. & Peyrin-Biroulet, L. Big data in IBD: a look into the future. Nat. Rev. Gastroenterol. Hepatol. 16, 312–321 (2019).

    PubMed  Google Scholar 

  107. Noor, E., Cherkaoui, S. & Sauer, U. Biological insights through omics data integration. Curr. Opin. Syst. Biol. 15, 39–47 (2019).

    Google Scholar 

  108. Lopez, C., Tcker, S., Salameh, T. & Tucker, C. An unsupervised machine learning method for discovering patient clusters based on genetic signatures. J. Biomed. Inform. 85, 30–39 (2018).

    PubMed  PubMed Central  Google Scholar 

  109. Shomorony, I. et al. An unsupervised learning approach to identify novel signatures of health and disease from multimodal data. Genome Med. 12, 7 (2020).

    PubMed  PubMed Central  Google Scholar 

  110. Price, N. D. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 35, 747–756 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Argelaguet, R. et al. Multi-omics factor analysis — a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).

    PubMed  PubMed Central  Google Scholar 

  112. Bisikirska, B. et al. Elucidation and pharmacological targeting of novel molecular drivers of follicular lymphoma progression. Cancer Res. 76, 664–674 (2016).

    CAS  PubMed  Google Scholar 

  113. Costello, J. C. et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32, 1202–1212 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Mezlini, A. M. & Goldenberg, A. Incorporating networks in a probabilistic graphical model to find drivers for complex human disease. PLoS Comput. Biol. 13, e1005580 (2017).

    PubMed  PubMed Central  Google Scholar 

  115. Fabris, F., Magalhaes, J. P. & Freitas, A. A. A review of supervised machine learning applied to ageing research. Biogerontology 18, 171–188 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Yu, Z. et al. Progressive semisupervised learning of multiple classifiers. IEEE Trans. Cybern. 48, 689–702 (2018).

    PubMed  Google Scholar 

  117. Huang, H., Vangay, P., McKinlay, C. E. & Knights, D. Multi-omics analysis of inflammatory bowel disease. Immunol. Lett. 162, 62–68 (2014).

    CAS  PubMed  Google Scholar 

  118. Lio, W. et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J. Med. Internet Res. 18, e323 (2016).

    Google Scholar 

  119. Doostparast Torshizi, A. & Petzold, L. R. Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification. J. Am. Med. Inform. Assoc. 25, 99–108 (2018).

    PubMed  Google Scholar 

  120. Lin, Y. et al. Computer-aided biomarker discovery for precision medicine: data resources, models and applications. Brief. Bioinform 20, 952–975 (2019).

    CAS  PubMed  Google Scholar 

  121. Tang, B., Pan, Z., Yin, K. & Khateeb, A. Recent advances of deep learning in bioinformatics and computational biology. Front. Genet. 10, 214 (2019).

    PubMed  PubMed Central  Google Scholar 

  122. Londhe, V. Y. & Bhasin, B. Artificial intelligence and its potential in oncology. Drug. Discov. Today 24, 228–232 (2019).

    PubMed  Google Scholar 

  123. Ngiam, K. Y. & Khor, I. W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 20, e262–e273 (2019).

    PubMed  Google Scholar 

  124. Babarenda Gamage, T. P. et al. An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment. Interface Focus. 9, 20190034 (2019).

    PubMed  PubMed Central  Google Scholar 

  125. Tseng, Y. J. et al. Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies. Int. J. Med. Inform. 128, 79–86 (2019).

    PubMed  Google Scholar 

  126. Goldenberg, S. L., Nir, G. & Salcudean, S. E. A new era: artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019).

    PubMed  Google Scholar 

  127. Paik, E. S. et al. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. J. Gynecol. Oncol. 30, e65 (2019).

    PubMed  PubMed Central  Google Scholar 

  128. Kouznetsova, V. L. et al. Recognition of early and late stages of bladder cancer using metabolites and machine learning. Metabolomics 15, 94 (2019).

    PubMed  Google Scholar 

  129. Jin, Y. et al. The diversity of gut microbiome is associated with favorable responses to anti-PD-1 immunotherapy in Chinese non-small cell lung cancer patients. J. Thorac. Oncol. 14, 1378–1389 (2019).

    CAS  PubMed  Google Scholar 

  130. Qian, Z. et al. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett. 451, 128–135 (2019).

    CAS  PubMed  Google Scholar 

  131. Leatherdale, S. T. & Lee, J. Artificial intelligence (AI) and cancer prevention: the potential application of AI in cancer control programming needs to be explored in population laboratories such as COMPASS. Cancer Causes Control. 30, 671–675 (2019).

    PubMed  Google Scholar 

  132. Veselkov, K. et al. HyperFoods: machine intelligent mapping of cancer-beating molecules in foods. Sci. Rep. 9, 9237 (2019).

    PubMed  PubMed Central  Google Scholar 

  133. Zhao, W. et al. Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning. Cancer Med. 8, 3532–3543 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Sato, M. et al. Machine-learning approach for the development of a novel predictive model for the diagnosis of hepatocellular carcinoma. Sci. Rep. 9, 7704 (2019).

    PubMed  PubMed Central  Google Scholar 

  135. Feng, Q. X. et al. An intelligent clinical decision support system for preoperative prediction of lymph node metastasis in gastric cancer. J. Am. Coll. Radiol. 16, 952–960 (2019).

    PubMed  Google Scholar 

  136. You, J., McLeod, R. D. & Hu, P. Predicting drug–target interaction network using deep learning model. Comput. Biol. Chem. 80, 90–101 (2019).

    CAS  PubMed  Google Scholar 

  137. Kessler, R. C., Bossarte, R. M., Luedtke, A., Zaslavsky, A. M. & Zubizarreta, J. R. Machine learning methods for developing precision treatment rules with observational data. Behav. Res. Ther. 120, 103412 (2019).

    PubMed  PubMed Central  Google Scholar 

  138. Mottini, C., Napolitano, F., Li, Z., Gao, X. & Cardone, L. Computer-aided drug repurposing for cancer therapy: approaches and opportunities to challenge anticancer targets. Semin. Cancer Biol. https://doi.org/10.1016/j.semcancer.2019.09.023 (2019).

    Article  PubMed  Google Scholar 

  139. Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).

    PubMed  PubMed Central  Google Scholar 

  140. Penson, A. et al. Development of genome-derived tumor type prediction to inform clinical cancer care. JAMA Oncol. https://doi.org/10.1001/jamaoncol.2019.3985 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Grewal, J. K. et al. Application of a neural network whole transcriptome-based pan-cancer method for diagnosis of primary and metastatic cancers. JAMA Netw. Open 2, e192597 (2019).

    PubMed  PubMed Central  Google Scholar 

  142. Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Vervier, K. et al. Large-scale machine learning for metagenomics sequence classification. Bioinformatics 32, 1023–1032 (2016).

    CAS  PubMed  Google Scholar 

  144. Fernandez-Navarro, T. et al. Exploring the interactions between serum free fatty acids and fecal microbiota in obesity through a machine learning algorithm. Food Res. Int. 121, 533–541 (2019).

    CAS  PubMed  Google Scholar 

  145. Thompson, J. et al. Machine learning to predict microbial community functions: an analysis of dissolved organic carbon from litter decomposition. PLoS One 14, e0215502 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Shinn, L. et al. Applying machine-learning to human gastrointestinal microbial species to predict dietary intake. Curr. Dev. Nutr. 3, https://doi.org/10.1093/cdn/nzz040.P20-040-19 (2019).

  147. Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project. Nature 569, 641-648.

  149. Vangav, P., Hillmann, B. M. & Knights, D. Microbiome learning repo (ML Repo): a public repository of microbiome regression and classification tasks. Gigascience 8, giz042 (2019).

    Google Scholar 

  150. Mallick., H. et al. Experimental design and quantitative analysis of microbial community multiomics. Genome Biol. 18, 228 (2017).

    PubMed  PubMed Central  Google Scholar 

  151. Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).

    CAS  PubMed  Google Scholar 

  152. Cani, P. D. Human gut microbiome: hopes, threats and promises. Gut 67, 1716–1725 (2018).

    CAS  PubMed  Google Scholar 

  153. Knights, D., Costello, E. K. & Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 35, 343–359 (2011).

    CAS  PubMed  Google Scholar 

  154. Moitinho-Silva, L. et al. Predicting the HMA–LMA status in marine sponges by machine learning. Front. Microbiol. 8, 752 (2017).

    PubMed  PubMed Central  Google Scholar 

  155. Bockulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl Med. 8, 343ra82 (2016).

    Google Scholar 

  156. Pasolli, E., Truong, D. T., Malik, F., Waldron, L. & Segata, N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput. Biol. 12, e1004977 (2016).

    PubMed  PubMed Central  Google Scholar 

  157. Heshiki, Y. et al. Predictable modulation of cancer treatment outcomes by the gut microbiota. Microbiome 8, 28 (2020).

    PubMed  PubMed Central  Google Scholar 

  158. Zuo, T. et al. Gut mucosal virome alterations in ulcerative colitis. Gut 68, 1169–1179 (2019).

    CAS  PubMed  Google Scholar 

  159. Larsen, P. E. & Dai, Y. Metabolome of human gut microbiome is predictive of host dysbiosis. Gigascience 4, 42 (2015).

    PubMed  PubMed Central  Google Scholar 

  160. Bokulich, N. et al. q2-sample-classifier: machine-learning tools for microbiome classification and regression. J. Open Source Softw. 3, 934 (2018).

    Google Scholar 

  161. Dhariwal, A. et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Edgar, R. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  PubMed  Google Scholar 

  163. Prifti, E. et al. Interpretable and accurate prediction models for metagenomics data. Gigascience 9, 1–11 (2020).

    Google Scholar 

  164. Zhou, Y.-H. & Gallins, P. A review and tutorial of machine learning methods for microbiome host trait prediction. Front. Genet. 10, 579 (2019).

    PubMed  PubMed Central  Google Scholar 

  165. Ananthakrishnan, A. et al. Gut microbiome function predicts response to anti-integrin biologic therapy in inflammatory bowel diseases. Cell Host Microbe 21, 603–610 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. Zhu, Q., Jiang, X., Zhu, Q., Pan, M. & He, T. Graph embedding deep learning guides microbial biomarkers’ identification. Front. Genet. 10, 1182 (2019).

    PubMed  PubMed Central  Google Scholar 

  167. Angermueller, C., Parnamaa, T., Parts, L. & Stegle, O. Deep learning for computational biology. Mol. Syst. Biol. 12, 878 (2016).

    PubMed  PubMed Central  Google Scholar 

  168. Eraslan, G., Avsec, Z., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).

    CAS  PubMed  Google Scholar 

  169. LaPierre, N., Ju, C. J., Zhou, G. & Wang, W. MetaPheno: a critical evaluation of deep learning and machine learning in metagenome-based disease prediction. Methods 166, 74–82 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  171. Stols-Goncalves, D. et al. Epigenetic markers and mMicrobiota/mMetabolite-induced epigenetic modifications in the pathogenesis of obesity, metabolic syndrome, type 2 diabetes, and non-alcoholic fatty liver disease. Curr. Diab. Rep. 19, 31 (2019).

    PubMed  PubMed Central  Google Scholar 

  172. Palsson, B. & Zengler, K. The challenges of integrating multi-omic data sets. Nat. Chem. Biol. 6, 787–789 (2010).

    PubMed  Google Scholar 

  173. Zitnik, M. et al. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Inf. Fusion. 50, 71–91 (2019).

    PubMed  Google Scholar 

  174. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Perkins, B. A. et al. Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults. Proc. Natl Acad. Sci. USA 115, 3685–3691 (2018).

    Google Scholar 

  176. Hollister, E. B. et al. Leveraging human microbiome features to diagnose and stratify children with irritable bowel syndrome. J. Mol. Diagn. 21, 449–461 (2019).

    PubMed  PubMed Central  Google Scholar 

  177. Kreznar, J. H. et al. Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes. Cell Rep. 18, 1739–1750 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  178. Schussler-Fiorenza Rose, S. M. et al. A longitudinal big data approach for precision health. Nat. Med. 25, 792–804 (2019).

    CAS  PubMed  Google Scholar 

  179. Flemer, B. et al. The oral microbiota in colorectal cancer is distinctive and predictive. Gut 67, 1454–1463 (2018).

    CAS  PubMed  Google Scholar 

  180. Zackular, J. P. et al. The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev. Res. 7, 1112–1121 (2014).

    CAS  Google Scholar 

  181. Imhann, F. et al. The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1. BMC Gastroenterol. 19, 5 (2019).

    PubMed  PubMed Central  Google Scholar 

  182. Casals-Pascual, C. et al. Microbial diversity in clinical microbiome studies: sample size and statistical power considerations. Gastroenterology 158, 1524–1528 (2020).

    PubMed  Google Scholar 

  183. Shenoi, S.J., Ly, V., Soni, S. & Roberts, K. Developing a serach engine for precision medicine. AMIA Jt. Summits Transl Sci. Proc. 2020, 579–588 (2020).

    PubMed  PubMed Central  Google Scholar 

  184. Goecks, J., Jalili, V., Heiser, L. M. & Gray, J. W. How machine learning will transform biomedicine. Cell 181, 92–101 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. Zheng, Y. et al. Specific gut microbiome signature predicts the early-stage lung cancer. Gut Microbes 11, 1–12 (2020).

    Google Scholar 

  186. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    CAS  PubMed  Google Scholar 

  187. Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  188. Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).

    CAS  PubMed  Google Scholar 

  189. Coburn, B. et al. Lung microbiota across age and disease stage in cystic fibrosis. Sci. Rep. 5, 10241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  190. Alekseyenko, A. V. et al. Community differentiation of the cutaneous microbiota in psoriasis. Microbiome 1, 31 (2013).

    PubMed  PubMed Central  Google Scholar 

  191. Wu, H. et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med. 23, 850–858 (2017).

    CAS  PubMed  Google Scholar 

  192. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  193. Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  194. Cussotto, S., Clarke, G., Dinan, T. G. & Cryan, J. F. Psychotropics and the microbiome: a chamber of secrets. Psychopharmacology 236, 1411–1432 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  195. Claesson, M. J. et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc. Natl Acad. Sci. USA 108, 4586–4591 (2011).

    CAS  PubMed  Google Scholar 

  196. Bibbo, S. et al. The role of diet on gut microbiota composition. Eur. Rev. Med. Pharmacol. Sci. 20, 4742–4749 (2016).

    CAS  PubMed  Google Scholar 

  197. Qiu, G. et al. The significance of probiotics in preventing radiotherapy-induced diarrhea in patients with cervical cancer: a systematic review and meta-analysis. Int. J. Surg. 65, 61–69 (2019).

    PubMed  Google Scholar 

  198. Liu, M. M. et al. Probiotics for prevention of radiation-induced diarrhea: a meta-analysis of randomized controlled trials. PLoS One 12, e0178870 (2017).

    PubMed  PubMed Central  Google Scholar 

  199. Wang, Y. H. et al. The efficacy and safety of probiotics for prevention of chemoradiotherapy-induced diarrhea in people with abdominal and pelvic cancer: a systematic review and meta-analysis. Eur. J. Clin. Nutr. 70, 1246–1253 (2016).

    PubMed  Google Scholar 

  200. Delia, P. et al. Use of probiotics for prevention of radiation-induced diarrhea. World J. Gastroenterol. 13, 912–915 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  201. Henson, C. C. et al. Nutritional interventions for reducing gastrointestinal toxicity in adults undergoing radical pelvic radiotherapy. Cochrane Database Syst. Rev. 11, CD009896 (2013).

    Google Scholar 

  202. Reyna-Figueroa, J. et al. Probiotic supplementation decreases chemotherapy-induced gastrointestinal side effects in patients with acute leukemia. J. Pediatr. Hematol. Oncol. 41, 468–472 (2019).

    CAS  PubMed  Google Scholar 

  203. Osterlund, P. et al. Lactobacillus supplementation for diarrhoea related to chemotherapy of colorectal cancer: a randomised study. Br. J. Cancer 97, 1028–1034 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  204. Wada, M. et al. Effects of the enteral administration of Bifidobacterium breve on patients undergoing chemotherapy for pediatric malignancies. Support. Care Cancer 18, 751–759 (2010).

    PubMed  Google Scholar 

  205. Tian, Y. et al. Effects of probiotics on chemotherapy in patients with lung cancer. Oncol. Lett. 17, 2836–2848 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  206. Mego, M. et al. Prevention of irinotecan induced diarrhea by probiotics: a randomized double blind, placebo controlled pilot study. Complement. Ther. Med. 23, 356–432 (2015).

    PubMed  Google Scholar 

  207. Chitapanarux, I. et al. Randomized controlled trial of live Lactobacillus acidophilus plus Bifidobacterium bifidum in prophylaxis of diarrhea during radiotherapy in cervical cancer patients. Radiat. Oncol. 5, 31 (2010).

    PubMed  PubMed Central  Google Scholar 

  208. Wei, D. et al. Probiotics for the prevention or treatment of chemotherapy- or radiotherapy-related diarrhoea in people with cancer. Cochrane Database Syst. Rev. 8, CD008831 (2018).

    PubMed  Google Scholar 

  209. Jonasch, E. et al. Phase II study of two weeks on, one week off sunitinib scheduling in patients with metastatic renal cell carcinoma. J. Clin. Oncol. 36, 1588–1593 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  210. Andreyev, J. et al. Guidance on the management of diarrhoea during cancer chemotherapy. Lancet Oncol. 15, e447–e460 (2014).

    PubMed  Google Scholar 

  211. Wang, Y. et al. Fecal microbiota transplantation for refractory immune checkpoint inhibitor-associated colitis. Nat. Med. 24, 1804–1808 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  212. Cammarota, G. et al. Fecal microbiota transplantation: a new old kid on the block for the management of gut microbiota-related disease. J. Clin. Gastroenterol. 48, S80–S84 (2014).

    PubMed  Google Scholar 

  213. O’Toole, P. W., Marchesi, J. R. & Hill, C. Next-generation probiotics: the spectrum from probiotics to live biotherapeutics. Nat. Microbiol. 2, 17057 (2017).

    PubMed  Google Scholar 

  214. Song, H. et al. Synthetic microbial consortia: from systematic analysis to construction and applications. Chem. Soc. Rev. 43, 6954–6981 (2014).

    CAS  PubMed  Google Scholar 

  215. Yuvaraj, S. et al. E. coli-produced BMP-2 as a chemopreventive strategy for colon cancer: a proof-of-concept study. Gastroenterol. Res. Pract. 2012, 895462 (2012).

    PubMed  PubMed Central  Google Scholar 

  216. Huibregtse, I. L. et al. Genetically modified Lactococcus lactis for delivery of human interleukin-10 to dendritic cells. Gastroenterol. Res. Pract. 2012, 639291 (2012).

    PubMed  Google Scholar 

  217. Pellegrini, M. et al. Gut microbiota composition after diet and probiotics in overweight breast cancer survivors: a randomized open-label pilot intervention trial. Nutrition 74, 110749 (2020).

    CAS  PubMed  Google Scholar 

  218. Moore, J. H. et al. Preparing next-generation scientists for biomedical big data: artificial intelligence approaches. Per. Med. 16, 247–257 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  219. Buruk, B., Ekmekci, P. E. & Arda, B. A critical perspective on guidelines for responsible and trustworthy artificial intelligence. Med. Health Care Philos. https://doi.org/10.1007/s11019-020-09948-1 (2020).

    Article  PubMed  Google Scholar 

  220. Price, W. N. II & Cohen, I. G. Privacy in the age of medical big data. Nat. Med. 25, 37–43 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  221. van den Bogert, B., Boekhorst, J., Provano, W. & May, A. On the role of bioinformatics and data science in industrial applications. Front. Genet. 10, 721 (2019).

    PubMed  PubMed Central  Google Scholar 

  222. Wang, Y. & Qian, P. Y. Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies. PLoS One 4, e7401 (2009).

    PubMed  PubMed Central  Google Scholar 

  223. Budding, A. E. et al. Automated broad-range molecular detection of bacteria in clinical samples. J. Clin. Microbiol. 54, 934–943 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  224. Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, e2329–e2338 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  225. Jin, P. et al. Mining the fecal proteome: from biomarkers to personalised medicine. Expert. Rev. Proteom. 14, 445–459 (2017).

    CAS  Google Scholar 

  226. Daliri, E. B. et al. The human microbiome and metabolomics: current concepts and applications. Crit. Rev. Food Sci. Nutr. 57, 3565–3576 (2017).

    CAS  PubMed  Google Scholar 

  227. Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469, 967–977 (2016).

    CAS  PubMed  Google Scholar 

  228. Vuik, F. et al. Composition of the mucosa-associated microbiota along the entire gastrointestinal tract of human individuals. United European Gastroenterol. J. 7, 897–907 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  229. Li, S. et al. Pancreatic cyst fluid harbors a unique microbiome. Microbiome 5, 147 (2016).

    CAS  Google Scholar 

Download references

Acknowledgements

This publication has in part emanated from research conducted with the financial support of AIRC Foundation for Cancer Research (AIRC IG grant number 18599, MFAG grant number 23681) and Science Foundation Ireland (grant number SFI/12/RC/2273).

Author information

Authors and Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Giovanni Cammarota.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Gastroenterology & Hepatology thanks M. Peppelenbosch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

ColPortal: https://colportal.imib.es

GenBank and Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/gds

IBD Multi-omics Database: http://ibdmdb.org/

Microbiome Learning Repo: https://knights-lab.github.io/MLRepo/

ML4Microbiome COST Action: https://www.cost.eu/actions/CA18131

ONCOBIOME Project: https://cordis.europa.eu/project/id/825410/it

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cammarota, G., Ianiro, G., Ahern, A. et al. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 17, 635–648 (2020). https://doi.org/10.1038/s41575-020-0327-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41575-020-0327-3

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer