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Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI

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  • Britton, R. A. et al. Taking microbiome science to the next level: recommendations to advance the emerging field of microbiome-based therapeutics and diagnostics. Gastroenterology 167, 1059–1064 (2024).

    CAS 

    Google Scholar     

  • Gerber, G. K. AI in microbiome research: where have we been, where are we going? Cell Host Microbe 32, 1230–1234 (2024).

    CAS 

    Google Scholar     

  • Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genom. Bioinforma. 3, lqab019 (2021).


    Google Scholar     

  • Lane, D. J. et al. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Natl Acad. Sci. USA 82, 6955–6959 (1985).

    CAS 

    Google Scholar     

  • Handelsman, J. Metagenomics: application of genomics to uncultured microorganisms. Microbiol. Mol. Biol. Rev. 68, 669–685 (2004).

    CAS 

    Google Scholar     

  • Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).

    CAS 

    Google Scholar     

  • Zhang, C. et al. Identification of low abundance microbiome in clinical samples using whole genome sequencing. Genome Biol. 16, 265 (2015).

    CAS 

    Google Scholar     

  • Shanahan, F., Ghosh, T. S. & O’Toole, P. W. Human microbiome variance is underestimated. Curr. Opin. Microbiol. 73, 102288 (2023).


    Google Scholar     

  • He, Y. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat. Med. 24, 1532–1535 (2018).

    CAS 

    Google Scholar     

  • Kennedy, K. M. et al. Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies. Nature 613, 639–649 (2023).

    CAS 

    Google Scholar     

  • de Goffau, M. C. et al. Recognizing the reagent microbiome. Nat. Microbiol. 3, 851–853 (2018).


    Google Scholar     

  • Whelan, F. J. et al. Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota. Nat. Microbiol. 5, 379–390 (2020).

    CAS 

    Google Scholar     

  • Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).


    Google Scholar     

  • McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).


    Google Scholar     

  • Nearing, J. T. et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat. Commun. 13, 342 (2022).

    CAS 

    Google Scholar     

  • Santiago, A. et al. Processing faecal samples: a step forward for standards in microbial community analysis. BMC Microbiol. 14, 112 (2014).


    Google Scholar     

  • Dore, J. E. et al. IHMS_SOP 06 V1: standard operating procedure for fecal samples DNA extraction, Protocol Q. International Human Microbiome Standards https://brd.nci.nih.gov/brd/sop/download-pdf/2525 (2015).

  • Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the comprehensive antibiotic resistance database. Nucleic Acids Res. 51, D690–D699 (2023).

    CAS 

    Google Scholar     

  • Spahn, C. et al. DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches. Commun. Biol. 5, 688 (2022).


    Google Scholar     

  • Schloss, P. D. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 9, e00525–18 (2018).


    Google Scholar     

  • Mirzayi, C. et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat. Med. 27, 1885–1892 (2021).

    CAS 

    Google Scholar     

  • Willis, A. D. & Clausen, D. S. Planning and describing a microbiome data analysis. Nat. Microbiol. 10, 604–607 (2025).

    CAS 

    Google Scholar     

  • Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).


    Google Scholar     

  • Valles-Colomer, M., Falony, G., Vieira-Silva, S. & Raes, J. Practical guidelines for gut microbiome analysis in microbiota-gut-brain axis research. Behav. Brain Sci. 42, e78 (2019).


    Google Scholar     

  • Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).

    CAS 

    Google Scholar     

  • Llorens-Rico, V., Vieira-Silva, S., Goncalves, P. J., Falony, G. & Raes, J. Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases. Nat. Commun. 12, 3562 (2021).

    CAS 

    Google Scholar     

  • Stammler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4, 28 (2016).


    Google Scholar     

  • Tang, G. et al. Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization. Preprint at BioRxiv https://doi.org/10.1101/2025.01.07.631807 (2025).


    Google Scholar     

  • McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. eLife 8, e46923 (2019).


    Google Scholar     

  • Sberro, H. et al. Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell 178, 1245–1259.e14 (2019).

    CAS 

    Google Scholar     

  • Abdill, R. J. et al. Integration of 168,000 samples reveals global patterns of the human gut microbiome. Cell 188, 1100–1118.e17 (2025).

    CAS 

    Google Scholar     

  • Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    CAS 

    Google Scholar     

  • Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2016).

    CAS 

    Google Scholar     

  • Vujkovic-Cvijin, I. et al. Host variables confound gut microbiota studies of human disease. Nature 587, 448–454 (2020).

    CAS 

    Google Scholar     

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

    CAS 

    Google Scholar     

  • Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Primers 2, https://doi.org/10.1038/s43586-021-00092-5 (2022).


    Google Scholar     

  • Liu, X. et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat. Genet. 54, 52–61 (2022).

    CAS 

    Google Scholar     

  • Bauermeister, A., Mannochio-Russo, H., Costa-Lotufo, L. V., Jarmusch, A. K. & Dorrestein, P. C. Mass spectrometry-based metabolomics in microbiome investigations. Nat. Rev. Microbiol. 20, 143–160 (2022).

    CAS 

    Google Scholar     

  • Aksenov, A. A., da Silva, R., Knight, R., Lopes, N. P. & Dorrestein, P. C. Global chemical analysis of biology by mass spectrometry. Nat. Rev. Chem. 1, 0054 (2017).

    CAS 

    Google Scholar     

  • Wang, M. et al. Mass spectrometry searches using MASST. Nat. Biotechnol. 38, 23–26 (2020).


    Google Scholar     

  • Galipeau, H. J. et al. Novel fecal biomarkers that precede clinical diagnosis of ulcerative colitis. Gastroenterology 160, 1532–1545 (2021).

    CAS 

    Google Scholar     

  • Shi, H. et al. Highly multiplexed spatial mapping of microbial communities. Nature 588, 676–681 (2020).

    CAS 

    Google Scholar     

  • Saarenpaa, S. et al. Spatial metatranscriptomics resolves host-bacteria-fungi interactomes. Nat. Biotechnol. 42, 1384–1393 (2024).


    Google Scholar     

  • Miller, G. A. The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956).

    CAS 

    Google Scholar     

  • Kline, R. Cybernetics, automata studies, and the dartmouth conference on artificial intelligence. IEEE Ann. Hist. Comput. 33, 5–16 (2011).


    Google Scholar     

  • Lambert, A., Budinich, M., Mahé, M., Chaffron, S. & Eveillard, D. Community metabolic modeling of host-microbiota interactions through multi-objective optimization. iScience 27, 110092 (2024).


    Google Scholar     

  • van der Ark, K. C. H., van Heck, R. G. A., Martins Dos Santos, V. A. P., Belzer, C. & de Vos, W. M. More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes. Microbiome 5, 78 (2017).


    Google Scholar     

  • Huang, B. D., Groseclose, T. M. & Wilson, C. J. Transcriptional programming in a bacteroides consortium. Nat. Commun. 13, 3901 (2022).

    CAS 

    Google Scholar     

  • van der Maaten, L. H. & Visualizing, G. Data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).


    Google Scholar     

  • McInnes, L., Healy, J., Saul, H. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).


    Google Scholar     

  • Smyth, J. et al. Microbiome-based colon cancer patient stratification and survival analysis. Cancer Med. 13, e70434 (2024).

    CAS 

    Google Scholar     

  • Mao, J. & Ma, L. I. Dirichlet-tree multinomial mixtures for clustering microbiome compositions. Ann. Appl. Stat. 16, 1476–1499 (2022).


    Google Scholar     

  • Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7, e30126 (2012).

    CAS 

    Google Scholar     

  • Backhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).


    Google Scholar     

  • Hua, J., Xiong, Z., Lowey, J., Suh, E. & Dougherty, E. R. Optimal number of features as a function of sample size for various classification rules. Bioinformatics 21, 1509–1515 (2005).

    CAS 

    Google Scholar     

  • Aguinis, H. & Harden, E. E. in Statistical and Methodological Myths and Urban Legends (ed Lance, C. E., Vandenberg, R. J.) Ch. 11 (Routledge, 2008).

  • Raygoza Garay, J. A. et al. Gut microbiome composition is associated with future onset of Crohn’s disease in healthy first-degree relatives. Gastroenterology 165, 670–681 (2023).

    CAS 

    Google Scholar     

  • Zheng, J. et al. Noninvasive, microbiome-based diagnosis of inflammatory bowel disease. Nat. Med. 30, 3555–3567 (2024).

    CAS 

    Google Scholar     

  • Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).

    CAS 

    Google Scholar     

  • Xiong, D. et al. A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02428-4 (2024).


    Google Scholar     

  • Jiang, K. et al. Rapid in silico directed evolution by a protein language model with EVOLVEpro. Science 387, eadr6006 (2024).


    Google Scholar     

  • Li, M. et al. Performance of gut microbiome as an independent diagnostic tool for 20 diseases: cross-cohort validation of machine-learning classifiers. Gut Microbes 15, 2205386 (2023).


    Google Scholar     

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

    CAS 

    Google Scholar     

  • Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2 edn (Springer, 2009).

  • Quinn-Bohmann, N. et al. Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut. Nat. Microbiol. 9, 1700–1712 (2024).

    CAS 

    Google Scholar     

  • Moore, L. R. et al. Revisiting the y-ome of Escherichia coli. Nucleic Acids Res. 52, 12201–12207 (2024).

    CAS 

    Google Scholar     

  • Box, G. E. P. Science and statistics. J. Am. Stat. Assoc. 71, 791–799 (1976).


    Google Scholar     

  • Wong, F. et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 626, 177–185 (2024).

    CAS 

    Google Scholar     

  • Fisch, D. et al. Defining host-pathogen interactions employing an artificial intelligence workflow. eLife 8, e40560 (2019).


    Google Scholar     

  • Sinha, S. et al. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nat. Cancer 5, 938–952 (2024).


    Google Scholar     

  • Sahoo, D. et al. Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease. Nat. Commun. 12, 4246 (2021).

    CAS 

    Google Scholar     

  • Santos, A. J. M. et al. A human autoimmune organoid model reveals IL-7 function in coeliac disease. Nature 632, 401–410 (2024).

    CAS 

    Google Scholar     

  • Rahmani, S. et al. Gluten-dependent activation of CD4+ T cells by MHC class II-expressing epithelium. Gastroenterology 167, 1113–1128 (2024).

    CAS 

    Google Scholar     

  • Li, C. G., Kreiman, G. & Ramanathan, S. Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks. Nat. Mach. Intell. https://doi.org/10.1038/s42256-024-00854-2 (2024).


    Google Scholar     

  • Vidovic, T., Dakhovnik, A., Hrabovskyi, O., MacArthur, M. R. & Ewald, C. Y. AI-predicted mTOR inhibitor reduces cancer cell proliferation and extends the lifespan of C. elegans. Int. J. Mol. Sci. 24, 7850 (2023).

    CAS 

    Google Scholar     

  • Vandamme, T. F. Use of rodents as models of human diseases. J. Pharm. Bioallied Sci. 6, 2–9 (2014).


    Google Scholar     

  • Khatri, P. et al. A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J. Exp. Med. 210, 2205–2221 (2013).

    CAS 

    Google Scholar     

  • Li, Y. et al. Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat. Commun. 13, 6891 (2022).

    CAS 

    Google Scholar     

  • Nordmann, T. M. et al. Spatial proteomics identifies JAKi as treatment for a lethal skin disease. Nature https://doi.org/10.1038/s41586-024-08061-0 (2024).


    Google Scholar     

  • Maslova, A. et al. Deep learning of immune cell differentiation. Proc. Natl Acad. Sci. USA 117, 25655–25666 (2020).

    CAS 

    Google Scholar     

  • Liu, G. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat. Chem. Biol. 19, 1342–1350 (2023).

    CAS 

    Google Scholar     

  • Luczynski, P. et al. Growing up in a bubble: using germ-free animals to assess the influence of the gut microbiota on brain and behavior. Int. J. Neuropsychopharmacol. 19, pyw020 (2016).


    Google Scholar     

  • Kennedy, E. A., King, K. Y. & Baldridge, M. T. Mouse microbiota models: comparing germ-free mice and antibiotics treatment as tools for modifying gut bacteria. Front. Physiol. 9, 1534 (2018).


    Google Scholar     

  • De Palma, G. et al. Histamine production by the gut microbiota induces visceral hyperalgesia through histamine 4 receptor signaling in mice. Sci. Transl. Med. 14, eabj1895 (2022).


    Google Scholar     

  • Galipeau, H. J. et al. Novel fecal biomarkers that precede clinical diagnosis of ulcerative colitis. Gastroenterology https://doi.org/10.1053/j.gastro.2020.12.004 (2020).


    Google Scholar     

  • Walter, J., Armet, A. M., Finlay, B. B. & Shanahan, F. Establishing or exaggerating causality for the gut microbiome: lessons from human microbiota-associated rodents. Cell 180, 221–232 (2020).

    CAS 

    Google Scholar     

  • Arrieta, M. C., Walter, J. & Finlay, B. B. Human microbiota-associated mice: a model with challenges. Cell Host Microbe 19, 575–578 (2016).

    CAS 

    Google Scholar     

  • Joos, R. et al. Examining the healthy human microbiome concept. Nat. Rev. Microbiol. 23, 192–205 (2025).

    CAS 

    Google Scholar     

  • NCD Risk Factor Collaboration (NCD-RisC). Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c. Nat. Med. 29, 2885–2901 (2023).


    Google Scholar     

  • International Expert Committee. International expert committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32, 1327–1334 (2009).


    Google Scholar     

  • Lilja, H., Ulmert, D. & Vickers, A. J. Prostate-specific antigen and prostate cancer: prediction, detection and monitoring. Nat. Rev. Cancer 8, 268–278 (2008).

    CAS 

    Google Scholar     

  • Mei, Z. et al. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes. Nat. Med. 30, 2265–2276 (2024).

    CAS 

    Google Scholar     

  • Lee, S. H. et al. Development and validation of an integrative risk score for future risk of Crohn’s disease in healthy first-degree relatives: a multicentre prospective cohort study. Gastroenterology https://doi.org/10.1053/j.gastro.2024.08.021 (2024).


    Google Scholar     

  • Gacesa, R. et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature 604, 732–739 (2022).

    CAS 

    Google Scholar     

  • Vatanen, T. et al. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature 562, 589–594 (2018).

    CAS 

    Google Scholar     

  • Savova, G. K. et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inf. Assoc. 17, 507–513 (2010).


    Google Scholar     

  • Porcari, S. et al. International consensus statement on microbiome testing in clinical practice. Lancet Gastroenterol. Hepatol. 10, 154–167 (2025).

    CAS 

    Google Scholar     

  • Mehta, R. S. et al. Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease. Nat. Med. 29, 700–709 (2023).

    CAS 

    Google Scholar     

  • Bredon, M. et al. Faecalibacterium prausnitzii is associated with clinical response to immune checkpoint inhibitors in patients with advanced gastric adenocarcinoma: results of microbiota analysis of PRODIGE 59-FFCD 1707-DURIGAST trial. Gastroenterology https://doi.org/10.1053/j.gastro.2024.10.020 (2024).


    Google Scholar     

  • Derosa, L. et al. Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome. Cell 187, 3373–3389.e16 (2024).

    CAS 

    Google Scholar     

  • Torres, M. D. T. et al. Mining human microbiomes reveals an untapped source of peptide antibiotics. Cell 187, 5453–5467.e15 (2024).

    CAS 

    Google Scholar     

  • Merenstein, D. et al. Emerging issues in probiotic safety: 2023 perspectives. Gut Microbes 15, 2185034 (2023).


    Google Scholar     

  • Yadegar, A. et al. Fecal microbiota transplantation: current challenges and future landscapes. Clin. Microbiol. Rev. 37, e0006022 (2024).


    Google Scholar     

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

    CAS 

    Google Scholar     

  • Ianiro, G. et al. Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases. Nat. Med. 28, 1913–1923 (2022).

    CAS 

    Google Scholar     

  • Lindenbaum, J., Rund, D. G., Butler, V. P. Jr., Tse-Eng, D. & Saha, J. R. Inactivation of digoxin by the gut flora: reversal by antibiotic therapy. N. Engl. J. Med. 305, 789–794 (1981).

    CAS 

    Google Scholar     

  • Dobkin, J. F., Saha, J. R., Butler, V. P. Jr., Neu, H. C. & Lindenbaum, J. Inactivation of digoxin by Eubacterium lentum, an anaerobe of the human gut flora. Trans. Assoc. Am. Physicians 95, 22–29 (1982).

    CAS 

    Google Scholar     

  • Koppel, N., Bisanz, J. E., Pandelia, M. E., Turnbaugh, P. J. & Balskus, E. P. Discovery and characterization of a prevalent human gut bacterial enzyme sufficient for the inactivation of a family of plant toxins. eLife 7, e33953 (2018).


    Google Scholar     

  • Amir, S., Kumar, M., Kumar, V. & Mohanty, D. HgutMgene-miner: in silico genome mining tool for deciphering the drug-metabolizing potential of human gut microbiome. Comput. Biol. Med. 186, 109679 (2025).

    CAS 

    Google Scholar     

  • Warraich, H. J., Tazbaz, T. & Califf, R. M. FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA https://doi.org/10.1001/jama.2024.21451 (2024).


    Google Scholar     

  • Tikkinen-Piri, C., Rohunen, A. & Markkula, J. EU general data protection regulation: changes and implications for personal data collecting companies. Computer Law Security Rev. 34, 134–153 (2018).


    Google Scholar     

  • Knoppers, B. M., Bernier, A., Bowers, S. & Kirby, E. Open data in the era of the GDPR: lessons from the human cell atlas. Annu. Rev. Genom. Hum. Genet. 24, 369–391 (2023).

    CAS 

    Google Scholar     

  • Huttenhower, C., Finn, R. D. & McHardy, A. C. Challenges and opportunities in sharing microbiome data and analyses. Nat. Microbiol. 8, 1960–1970 (2023).

    CAS 

    Google Scholar     

  • Chen, R. J. et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat. Biomed. Eng. 7, 719–742 (2023).


    Google Scholar     

  • Chen, Y., Clayton, E. W., Novak, L. L., Anders, S. & Malin, B. Human-centered design to address biases in artificial intelligence. J. Med. Internet Res. 25, e43251 (2023).


    Google Scholar     

  • Probul, N., Huang, Z., Saak, C. C., Baumbach, J. & List, M. AI in microbiome-related healthcare. Microb. Biotechnol. 17, e70027 (2024).


    Google Scholar     

  • Dixon, P., Horton, R. H., Newman, W. G., McDermott, J. H. & Lucassen, A. Genomics and insurance in the United Kingdom: increasing complexity and emerging challenges. Health Econ. Policy Law 19, 446–458 (2024).


    Google Scholar     

  • Qin, N. et al. Alterations of the human gut microbiome in liver cirrhosis. Nature 513, 59–64 (2014).

    CAS 

    Google Scholar     

  • 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 

    Google Scholar     

  • Clooney, A. G. et al. Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study. Gut 70, 499–510 (2021).

    CAS 

    Google Scholar     

  • Ruppe, E. et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nat. Microbiol. 4, 112–123 (2019).

    CAS 

    Google Scholar     

  • Swarte, J. C. et al. Multiple indicators of gut dysbiosis predict all-cause and cause-specific mortality in solid organ transplant recipients. Gut 73, 1650–1661 (2024).


    Google Scholar     

  • Caenepeel, C. et al. Dysbiosis and associated stool features improve prediction of response to biological therapy in inflammatory bowel disease. Gastroenterology 166, 483–495 (2024).


    Google Scholar     

  • Su, Q. et al. Faecal microbiome-based machine learning for multi-class disease diagnosis. Nat. Commun. 13, 6818 (2022).

    CAS 

    Google Scholar     

  • Leibovitzh, H. et al. Altered gut microbiome composition and function are associated with gut barrier dysfunction in healthy relatives of patients with Crohn’s disease. Gastroenterology 163, 1364–1376.e10 (2022).

    CAS 

    Google Scholar     

  • Vieira-Silva, S. et al. Quantitative microbiome profiling disentangles inflammation- and bile duct obstruction-associated microbiota alterations across PSC/IBD diagnoses. Nat. Microbiol. 4, 1826–1831 (2019).

    CAS 

    Google Scholar     

  • Saulnier, D. M. et al. Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome. Gastroenterology 141, 1782–1791 (2011).

    CAS 

    Google Scholar     

  • Ren, Z. et al. Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma. Gut 68, 1014–1023 (2019).

    CAS 

    Google Scholar     

  • Nagata, N. et al. Metagenomic identification of microbial signatures predicting pancreatic cancer from a multinational study. Gastroenterology 163, 222–238 (2022).

    CAS 

    Google Scholar     

  • Zafeiropoulou, K. et al. Alterations in intestinal microbiota of children with celiac disease at the time of diagnosis and on a gluten-free diet. Gastroenterology 159, 2039–2051.e20 (2020).

    CAS 

    Google Scholar     

  • Leung, H. et al. Risk assessment with gut microbiome and metabolite markers in NAFLD development. Sci. Transl. Med. 14, eabk0855 (2022).

    CAS 

    Google Scholar     

  • Tap, J. et al. Identification of an intestinal microbiota signature associated with severity of irritable bowel syndrome. Gastroenterology 152, 111–123.e8 (2017).


    Google Scholar     

  • Armstrong, G. et al. Uniform manifold approximation and projection (UMAP) reveals composite patterns and resolves visualization artifacts in microbiome data. mSystems 6, e0069121 (2021).


    Google Scholar     

  • Xu, X., Xie, Z., Yang, Z., Li, D. & Xu, X. A t-SNE based classification approach to compositional microbiome data. Front. Genet. 11, 620143 (2020).


    Google Scholar     

  • Ferreira, R. M. et al. Gastric microbial community profiling reveals a dysbiotic cancer-associated microbiota. Gut 67, 226–236 (2018).

    CAS 

    Google Scholar     

  • Loomba, R. et al. Gut microbiome-based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease. Cell Metab. 25, 1054–1062.e5 (2017).

    CAS 

    Google Scholar     

  • Oh, T. G. et al. A universal gut-microbiome-derived signature predicts cirrhosis. Cell Metab. 32, 901 (2020).

    CAS 

    Google Scholar     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    CAS 

    Google Scholar     

  • Ma, Y. et al. Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat. Biotechnol. 40, 921–931 (2022).

    CAS 

    Google Scholar     

  • Berry, S. E. et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26, 964–973 (2020).

    CAS 

    Google Scholar     

  • Weis, C. et al. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nat. Med. 28, 164–174 (2022).

    CAS 

    Google Scholar     

  • Nishijima, S. et al. Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations. Cell 188, 222–236.e15 (2025).

    CAS 

    Google Scholar     

  • Constante, M. et al. Biogeographic variation and functional pathways of the gut microbiota in celiac disease. Gastroenterology 163, 1351–1363.e15 (2022).

    CAS 

    Google Scholar     

  • Shalon, D. et al. Profiling the human intestinal environment under physiological conditions. Nature 617, 581–591 (2023).

    CAS 

    Google Scholar     

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

    CAS 

    Google Scholar     

  • Wilmanski, T. et al. Heterogeneity in statin responses explained by variation in the human gut microbiome. Med 3, 388–405.e6 (2022).

    CAS 

    Google Scholar     

  • Feuerstadt, P. et al. SER-109, an oral microbiome therapy for recurrent clostridioides difficile infection. N. Engl. J. Med. 386, 220–229 (2022).

    CAS 

    Google Scholar     

  • She, J. et al. Statins aggravate insulin resistance through reduced blood glucagon-like peptide-1 levels in a microbiota-dependent manner. Cell Metab. 36, 408–421.e5 (2024).

    CAS 

    Google Scholar     

  • Pandi, A. et al. Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides. Nat. Commun. 14, 7197 (2023).

    CAS 

    Google Scholar     

  • Caschera, F. Bacterial cell-free expression technology to in vitro systems engineering and optimization. Synth. Syst. Biotechnol. 2, 97–104 (2017).


    Google Scholar     

  • Huang, Y. et al. High-throughput microbial culturomics using automation and machine learning. Nat. Biotechnol. 41, 1424–1433 (2023).

    CAS 

    Google Scholar     

  • Ng, K. M. et al. Single-strain behavior predicts responses to environmental pH and osmolality in the gut microbiota. mBio 14, e0075323 (2023).


    Google Scholar     

  • Leao, T. F. et al. NPOmix: a machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters. PNAS Nexus 1, pgac257 (2022).


    Google Scholar     

  • Nguyen, H. A. et al. Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra. mSystems 9, e0078924 (2024).


    Google Scholar     

  • Boeckaerts, D. et al. Prediction of Klebsiella phage-host specificity at the strain level. Nat. Commun. 15, 4355 (2024).

    CAS 

    Google Scholar     

  • Raajaraam, L. & Raman, K. Modeling microbial communities: perspective and challenges. ACS Synth. Biol. 13, 2260–2270 (2024).

    CAS 

    Google Scholar     

  • Ghannam, R. B. & Techtmann, S. M. Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring. Comput. Struct. Biotechnol. J. 19, 1092–1107 (2021).

    CAS 

    Google Scholar     

  • Hashizume, T., Ozawa, Y. & Ying, B. W. Employing active learning in the optimization of culture medium for mammalian cells. NPJ Syst. Biol. Appl. 9, 20 (2023).

    CAS 

    Google Scholar     

  • Bai, L. et al. AI-enabled organoids: construction, analysis, and application. Bioact. Mater. 31, 525–548 (2024).


    Google Scholar     

  • Kong, J. et al. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat. Commun. 11, 5485 (2020).

    CAS 

    Google Scholar     

  • Michel-Mata, S., Wang, X. W., Liu, Y. Y. & Angulo, M. T. Predicting microbiome compositions from species assemblages through deep learning. iMeta 1, e3 (2022).

    CAS 

    Google Scholar     

  • Kang, D. & Douglas, A. E. Functional traits of the gut microbiome correlated with host lipid content in a natural population of Drosophila melanogaster. Biol. Lett. 16, 20190803 (2020).

    CAS 

    Google Scholar     

  • Maltecca, C. et al. Predicting growth and carcass traits in swine using microbiome data and machine learning algorithms. Sci. Rep. 9, 6574 (2019).


    Google Scholar     

  • Kuthyar, S., Manus, M. B. & Amato, K. R. Leveraging non-human primates for exploring the social transmission of microbes. Curr. Opin. Microbiol. 50, 8–14 (2019).


    Google Scholar     

  • Bisanz, J. E., Upadhyay, V., Turnbaugh, J. A., Ly, K. & Turnbaugh, P. J. Meta-analysis reveals reproducible gut microbiome alterations in response to a high-fat diet. Cell Host Microbe 26, 265–272.e4 (2019).

    CAS 

    Google Scholar     

  • Caminero, A. et al. Duodenal bacteria from patients with celiac disease and healthy subjects distinctly affect gluten breakdown and immunogenicity. Gastroenterology 151, 670–683 (2016).

    CAS 

    Google Scholar     

  • Dey, N. et al. Regulators of gut motility revealed by a gnotobiotic model of diet-microbiome interactions related to travel. Cell 163, 95–107 (2015).

    CAS 

    Google Scholar     

  • Bucci, V. et al. MDSINE: microbial dynamical systems INference engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).


    Google Scholar     

  • Schreiner M. OpenAI’s ChatGPT app for Android is now available. The Decoder https://the-decoder.com/openai-launches-chatgpt-app-for-android (updated 25 July 2023).



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    AI Research

    Causaly Introduces First Agentic AI Platform Built for Life Sciences Research and Development

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    September 16, 2025

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    Specialized AI agents automate research workflows and accelerate
    drug discovery and development with transparent, evidence-backed insights

    LONDON, Sept. 16, 2025 /PRNewswire/ — Causaly today introduced Causaly Agentic Research, an agentic AI breakthrough that delivers the transparency and scientific rigor that life sciences research and development demands. First-of-their-kind, specialized AI agents access, analyze, and synthesize comprehensive internal and external biomedical knowledge and competitive intelligence. Scientists can now automate complex tasks and workflows to scale R&D operations, discover novel insights, and drive faster decisions with confidence, precision, and clarity.

    Industry-specific scientific AI agents

    Causaly Agentic Research builds on Causaly Deep Research with a conversational interface that lets users interact directly with Causaly AI research agents. Unlike legacy literature review tools and general-purpose AI tools, Causaly Agentic Research uses industry-specific AI agents built for life sciences R&D and securely combines internal and external data to create a single source of truth for research. Causaly AI agents complete multi-step tasks across drug discovery and development, from generating and testing hypotheses to producing structured, transparent results always backed by evidence.

    “Agentic AI fundamentally changes how life sciences conducts research,” said Yiannis Kiachopoulos, co-founder and CEO of Causaly. “Causaly Agentic Research emulates the scientific process, automatically analyzing data, finding biological relationships, and reasoning through problems. AI agents work like digital assistants, eliminating manual tasks and dependencies on other teams, so scientists can access more diverse evidence sources, de-risk decision-making, and focus on higher-value work.”

    Solving critical research challenges

    Research and development teams need access to vast amounts of biomedical data, but manual and siloed processes slow research and create long cycle times for getting treatments to market. Scientists spend weeks analyzing narrow slices of data while critical insights remain hidden. Human biases influence decisions, and the volume of scientific information overwhelms traditional research approaches.

    Causaly addresses these challenges as the first agentic AI platform for scientists that combines extensive biomedical information with competitive intelligence and proprietary datasets. With a single, intelligent interface for scientific discovery that fits within scientists’ existing workflows, research and development teams can eliminate silos, improve productivity, and accelerate scientific ideas to market.

    Comprehensive agentic AI research platform

    As part of the Causaly platform, Causaly Agentic Research provides scientists multiple AI agents that collaborate to:

    • Conduct complex analysis and provide answers that move research forward
    • Verify quality and accuracy to dramatically reduce time-to-discovery
    • Continuously scan the scientific landscape to surface critical signals and emerging evidence in real time
    • Deliver fully traceable insights that help teams make confident, evidence-backed decisions while maintaining scientific rigor for regulatory approval
    • Connect seamlessly with internal systems, public applications, data sources, and even other AI agents, unifying scientific discovery

    Availability

    Causaly Agentic Research will be available in October 2025, with a conversational interface and foundational AI agents to accelerate drug discovery and development. Additional specialized AI agents are planned for availability by the end of the year.

    Explore how Causaly Agentic Research can redefine your R&D workflows and bring the future of drug development to your organization at causaly.com/products/agentic-research.

    About Causaly

    Causaly is a leader in AI for the life sciences industry. Leading biopharmaceutical companies use the Causaly AI platform to find, visualize, and interpret biomedical knowledge and automate critical research workflows. To learn how Causaly is accelerating drug discovery through transformative AI technologies and getting critical treatments to patients faster, visit www.causaly.com.

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    AI Research

    Josh Bersin Company Research Reveals How Talent Acquisition Is Being Revolutionized by AI

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    49 minutes ago

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    September 16, 2025

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    • Jobs aren’t disappearing. Through AI, talent acquisition is fast evolving from hand-crafted interviewing and recruiting to a data-driven model that ensures the right talent is hired at the right time, for the right role with unmatched accuracy

    • Traditional recruiting isn’t working: in 2024, only 17% of applicants received interviews and 60% abandoned slow application processes

    • AI drives 2–3x faster hiring, stronger candidate quality, sharper targeting—and 95% candidate satisfaction at Foundever, from 200,000+ applicants in just six months

    OAKLAND, Calif., Sept. 16, 2025 /PRNewswire/ — The Josh Bersin Company, the world’s most trusted HR advisory firm, today released new research showing that jobs aren’t disappearing—they’re being matched with greater intelligence. The research, produced in collaboration with AMS, reveals major advances in talent acquisition (TA) driven by AI-enabled technology, which are yielding 2–3x faster time to hire, stronger candidate-role matches, and unprecedented precision in sourcing.

    The Josh Bersin Company (PRNewsfoto/The Josh Bersin Company)

    The global market for recruiting, hiring, and staffing is over $850 billion and is growing at 13% per year, despite the economic slowdown, though signs of strain are evident. This means TA leaders are turning to AI to adapt, as AI transforms jobs, creates the need for new roles, new skills, and AI expertise.

    According to the research and advisory firm, even without AI disruption, over 20% of employees consider changing jobs each year, driving demand for a new wave of high-precision, AI-powered tools for assessment, interviewing, selection, and hiring. Companies joining this AI revolution are hiring 200-300% faster, with greater accuracy and efficiency than their peers, despite the job market slowdown.

    According to the report, The Talent Acquisition Revolution: How AI is Transforming Recruiting, the TA automation revolution is delivering benefits across the hiring ecosystem: job seekers experience faster recognition and better fit, while employers gain accurate, real-time, and highly scalable recruitment.

    This is against a context of failure with current hiring. In 2024, less than one in four (17%) of applicants made it to the interview stage, and 60% of job seekers, due to too-slow hiring portals, abandoned the whole application process.

    The research shows how organizations are already realizing benefits such as lower hiring costs, stronger internal mobility, and higher productivity. AI-empowered TA teams are also streamlining operations by shifting large portions of manual, admin-heavy work to specialized vendors.

    Early successes are striking: after deploying conversational AI, a major U.S. resort operator increased scheduled interviews by 423% in 12 months while reducing candidate drop-off by 85%. A new AI TA process at Foundever hit 95% candidate satisfaction rating from over 200,000 applicants in just six months, while a leading global automotive company reported $2 million in savings in its first year using AI-powered interview scheduling.

    The research highlights how AI is helping TA overcome frustrations like vague job descriptions, inconsistent interviews, and labor-intensive processes.

    HR teams now use AI to automate tasks from profiling and sourcing to screening, scheduling, interviewing, and negotiating offers. Companies showcasing these successes include major international brands spanning the hospitality, food, healthcare, and technology sectors, such as Fontainebleau Las Vegas and Compass Group.

    Some organizations are using AI-powered recruiting assistants to manage routine communication and negotiations. Recruiters set parameters for salary, benefits, and start dates, while the AI answers questions, updates offers, and proposes alternatives in real time. By automating tasks and personalizing the experience, AI shortens turnaround times and frees recruiters to focus on strategy.

    The vendor market is transforming, with SAP acquiring SmartRecruiters and Workday acquiring Paradox to stay competitive. Innovations include AI agents enabling automation of the full hiring journey (Eightfold AI, Maki People) conversational AI for candidate engagement (Glider AI, Paradox, Radancy), AI-driven assessments (CodeSignal, HireVue, TaTiO), and platforms that benchmark roles against labor market data (Draup, Galileo, Lightcast, Reejig, SeekOut, TechWolf).

    Report author and Josh Bersin Company Industry Analyst & Senior Research Director, Stella Ioannidou, says:

    “For decades, TA has been viewed as a cost center, focused on using applicant tracking systems to manage incoming candidates and relying on recruiters to screen and interview. This process was slow, expensive, and delivered a poor experience for job seekers.

    “Today, by leveraging AI-powered platforms and integrated data, TA teams can identify, attract, and engage the best talent in the market with unparalleled precision, often before competitors even know those candidates are available—resulting in a proactive, data-driven approach that enables organizations to respond quickly to changing business needs, seize new opportunities, and fuel growth from within.”

    Chief Executive Officer at AMS, Gordon Stuart, says:

    “This research paper captures the urgency and scale of the AI revolution which is transforming TA. It doesn’t just reflect what’s happening now, it helps us understand what’s next. Candidates want speed, clarity, and connection. Recruiters need tools that free them to focus on strategy and relationships. And businesses must rethink how talent acquisition fits into their broader growth agenda. AI is not just automating tasks, it’s redefining roles, workflows, and expectations across the board. The cumulative power of people, process, data and technology is heralding a new era for talent acquisition.”

    Global industry analyst and Josh Bersin Company CEO, Josh Bersin, says:

    “AI is expected to provide CHROs with a data-rich view of talent comparable to an integrated supply chain, enabling them to track and analyze every detail of each hire with the same precision a luxury Swiss watchmaker applies to every component and its origin.

    “This transition transforms HR from handcrafted processes to precision, dynamic hiring—something once unattainable without AI.

    “The implications are significant for all stakeholders, particularly CEOs. Organizations that lacked a precision hiring process have historically faced millions in lost revenue, high turnover, and misaligned talent—but those days are now coming to an end.”

    This new research follows previous Josh Bersin Company findings demonstrating how AI is transforming another core HR function: Learning & Development.

    The Josh Bersin Company’s Galileo Suite delivers both strategic guidance and hands-on tools through its AI Agent for HR, as well as hyper-personalized learning through the exclusive Galileo Learn certificate course, AI-First TA Transformation. The full report is available to download here.

    About AMS
    AMS is a leading global provider of talent acquisition and consulting services, providing unrivalled experience, driven by technology and underpinned by innovation. We help our clients to attract, engage and retain the talent they need for business success.

    We have three core areas of service: acquisition, advisory and digital, mainly delivered as an outsourced model, and spanning our clients’ permanent and contingent workforce, and internal mobility requirements.

    Our dedicated teams of experts are deeply embedded with our global blue-chip clients, enhancing talent acquisition processes and driving projects which align with overall strategic objectives. This relationship-driven approach is supporting our clients to redefine how they hire and retain top talent. For more, go to www.weareams.com/

    About The Josh Bersin Company
    The most trusted human capital advisors in the world. More than a million HR and business leaders rely on us to help them overcome their greatest people challenges.

    Thanks to our understanding of workplace issues, informed by the largest and most up-to-date data sets on workers and employees, we give leaders the confidence to make decisions in line with the latest thinking and evidence about work and the workplace. We’re great listeners, too. There’s no one like us, who understands this area so comprehensively and without bias.

    Our offerings include the industry’s leading AI-powered HR expert assistant, Galileo®, fueled by 25 years of in-depth Josh Bersin Company research, case studies, benchmarks, and market information.

    We help CHROs and CEOs be better at delivering their business goals. We do that by helping you to manage people better. We are enablers at our core. We provide strategic advice and counsel supported by in-depth research, thought leadership, and unrivaled professional development, community, and networking opportunities.

    We empower our clients to run their businesses better. And we empower the market by identifying results-driven practices that make work better for every person on the planet.

    Cision
    Cision

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    AI Research

    How AI-trained robots are helping to root out fake paintings tied to a notorious forgery case – The Art Newspaper

    Published

    50 minutes ago

    on

    September 16, 2025

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    Artificial intelligence (AI) is often criticised for ripping off artists, but the technology is now being used to combat fake copies of works by the Canadian artist Norval Morrisseau (1932-2007) that have flooded the market over the past two decades.

    More than 6,000 pieces were produced and fraudulently sold as authentic works by the Ojibwe artist to collectors worldwide, with financial losses estimated to exceed C$100m ($72.5m). The trial of Jeffrey Cowan, the last of the suspected fraudsters in the tangled web of Morrisseau forgery rings, began this week. Two other men who pleaded guilty to participating in what the Ontario Provincial Police described as “the biggest art fraud in world history”, were each given a conditional sentence of two years less a day in August and September.

    In a unique case of fighting fakes with fakes, the Montreal-based start-up Acrylic Robotics announced in July that, in partnership with the Norval Morrisseau Estate, its robots will reproduce five Morrisseau paintings and make the copies available for purchase.

    Norval AI

    “We have created our own artificial intelligence called Norval AI to help determine the probability of an authentic Norval Morrisseau painting,” Cory Dingle, the Morrisseau estate’s executive director, tells The Art Newspaper. “It has grown to do many other functions that will help with museums seeking provenance as well as law enforcement—such as catching the person who painted the fraudulent work.”

    The idea of developing an AI authenticator began in 2023, when Dingle met Stephan Heblich, an economics professor at the University of Toronto, and Clément Gorin, an associate professor at the Université Paris 1 Panthéon-Sorbonne.

    “Clément and I were just two art-loving economics professors who are using the latest deep learning and visual recognition techniques to analyse paintings,” Heblich says. “We thought we could help restore Norval’s legacy, so we reached out to Cory and created Norval AI.”

    A year later, Dingle met Chloë Ryan, the founder and chief executive of Acrylic Robotics. The artist-turned-engineer had developed technology that allows robots to paint works in the style of individual artists.

    Better fakes

    “We needed better replicas to test our artificial intelligence programme—the existing fake paintings were so terrible,” Dingle says. “So we collaborated with Acrylic Robotics and helped train their robot to paint more realistic fake paintings.” He adds: “They want to produce very accurate reproductions and our Norval AI tells their robot where it is doing a bad job. They use our data to make the robot do a better job, which makes those better replicas train our Norval AI even better.”

    Last year, the Norval Morrisseau Family Foundation helped Acrylic Robotics produce a very accurate replica of one of Morrisseau’s original paintings. This in turn was run through the Norval AI programme and the results were shared with Acrylic Robotics to help improve its replicas.

    The resulting works produced by Acrylic Robotics include limited editions of five paintings, including Morrisseau’s In Honour of Native Motherhood (1990), which was inspired by the murdered and missing Indigenous women in Canada, and Punk Rockers (around 1991), in which Morrisseau fused traditional Anishinaabe iconography with contemporary idioms.

    Marked as replicas

    Prices for the Acrylic Robotics works range from C$3,240 to C$45,000. To avoid further fraud, several techniques have been applied to ensure the works are easily identifiable as replicas, including a mark on the back of the canvas.

    According to an Acrylic Robotics spokesperson, the company hopes to investigate whether, with the support of Norval AI, its robots may be able to complete some of Morrisseau’s many unfinished or damaged works.

    “What’s exciting here, even beyond the technology,” Ryan says, “is the opportunity to push the boundaries of what has been possible, make art history and reclaim a legacy.”



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