{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:25:40Z","timestamp":1772166340931,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T00:00:00Z","timestamp":1602028800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T00:00:00Z","timestamp":1602028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and Results<\/jats:title>\n                    <jats:p>\n                      To identify the microbial interaction network, the\n                      <jats:italic>zero-inflated latent Ising<\/jats:italic>\n                      (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13040-020-00226-7","type":"journal-article","created":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T05:05:57Z","timestamp":1602047157000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identification of microbial interaction network: zero-inflated latent Ising model based approach"],"prefix":"10.1186","volume":"13","author":[{"given":"Jie","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weston D.","family":"Viles","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boran","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juliette C.","family":"Madan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margaret R.","family":"Karagas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Gui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0617-0196","authenticated-orcid":false,"given":"Anne G.","family":"Hoen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"issue":"8","key":"226_CR1","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1038\/nrmicro2832","volume":"10","author":"K Faust","year":"2012","unstructured":"Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012; 10(8):538\u201350. https:\/\/doi.org\/10.1038\/nrmicro2832.","journal-title":"Nat Rev Microbiol"},{"key":"226_CR2","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1146\/annurev-statistics-010814-020351","volume":"2","author":"HZ Li","year":"2015","unstructured":"Li HZ. Microbiome, metagenomics, and high-dimensional compositional data analysis. Ann Rev Stat Appl. 2015; 2:73\u201394. https:\/\/doi.org\/10.1146\/annurev-statistics-010814-020351.","journal-title":"Ann Rev Stat Appl"},{"issue":"Suppl 1","key":"226_CR3","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1111\/j.1753-4887.2012.00493.x","volume":"70","author":"LK Ursell","year":"2012","unstructured":"Ursell LK, Metcalf JL, Parfrey LW, Knight R. Defining the human microbiome. Nutr Rev. 2012; 70(Suppl 1):38\u201344. https:\/\/doi.org\/10.1111\/j.1753-4887.2012.00493.x.","journal-title":"Nutr Rev"},{"key":"226_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1038\/345063a0","volume":"345","author":"D Ward","year":"1990","unstructured":"Ward D, Weller R, Bateson M. 16S rRNA sequences reveal numerous uncultured microorganisms in a natural community. Nature. 1990; 345:63\u20135. https:\/\/doi.org\/10.1038\/345063a0.","journal-title":"Nature"},{"issue":"10","key":"226_CR5","doi-asserted-by":"publisher","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","volume":"11","author":"S Anders","year":"2010","unstructured":"Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010; 11(10):R106. https:\/\/doi.org\/10.1186\/gb-2010-11-10-r106.","journal-title":"Genome Biol"},{"key":"226_CR6","doi-asserted-by":"publisher","first-page":"e4600","DOI":"10.7717\/peerj.4600","volume":"6","author":"L Chen","year":"2018","unstructured":"Chen L, Reeve J, Zhang L, Huang S, Wang X, Chen J. GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ. 2018; 6:e4600. https:\/\/doi.org\/10.7717\/peerj.4600.","journal-title":"PeerJ"},{"key":"226_CR7","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.3389\/fmicb.2017.02224","volume":"8","author":"GB Gloor","year":"2017","unstructured":"Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017; 8:2224. https:\/\/doi.org\/10.3389\/fmicb.2017.02224.","journal-title":"Front Microbiol"},{"issue":"3","key":"226_CR8","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.cell.2012.10.012","volume":"151","author":"J Lov\u00e9n","year":"2012","unstructured":"Lov\u00e9n J, Orlando DA, Sigova AA, Lin CY, Rahl PB, Burge CB, Levens DL, Lee TI, Young RA. Revisiting global gene expression analysis. Cell. 2012; 151(3):476\u201382.","journal-title":"Cell"},{"issue":"1","key":"226_CR9","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","volume":"26","author":"MD Robinson","year":"2010","unstructured":"Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1):139\u201340. https:\/\/doi.org\/10.1093\/bioinformatics\/btp616.","journal-title":"Bioinformatics"},{"issue":"2","key":"226_CR10","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/j.2517-6161.1982.tb01195.x","volume":"44","author":"J Aitchison","year":"1982","unstructured":"Aitchison J. The statistical analysis of compositional data. J R Stat Soc Ser B Methodol. 1982; 44(2):139\u201360.","journal-title":"J R Stat Soc Ser B Methodol"},{"issue":"3","key":"226_CR11","doi-asserted-by":"publisher","first-page":"e1004075","DOI":"10.1371\/journal.pcbi.1004075","volume":"11","author":"D Lovell","year":"2015","unstructured":"Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, B\u00e4hler J. Proportionality: a valid alternative to correlation for relative data. PLoS Comput Biol. 2015; 11(3):e1004075. https:\/\/doi.org\/10.1371\/journal.pcbi.1004075.","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"226_CR12","doi-asserted-by":"publisher","first-page":"27663","DOI":"10.3402\/mehd.v26.27663","volume":"26","author":"S Mandal","year":"2015","unstructured":"Mandal S, Van Treuren W, White RA, Eggesb\u00f8 M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015; 26(1):27663. https:\/\/doi.org\/10.3402\/mehd.v26.27663.","journal-title":"Microb Ecol Health Dis"},{"issue":"1","key":"226_CR13","doi-asserted-by":"publisher","first-page":"e0016216","DOI":"10.1128\/mSystems.00162-16","volume":"2","author":"JT Morton","year":"2017","unstructured":"Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vazquez-Baeza Y, Navas-Molina JA, Song SJ, Metcalf JL, Hyde ER, Lladser M, Dorrestein PC, Knight R. Balance trees reveal microbial niche differentiation. MSystems. 2017; 2(1):e0016216. https:\/\/doi.org\/10.1128\/msystems.00162-16.","journal-title":"MSystems"},{"issue":"5","key":"226_CR14","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.annepidem.2016.03.002","volume":"26","author":"MC Tsilimigras","year":"2016","unstructured":"Tsilimigras MC, Fodor AA. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann Epidemiol. 2016; 26(5):330\u20135. https:\/\/doi.org\/10.1016\/j.annepidem.2016.03.002.","journal-title":"Ann Epidemiol"},{"key":"226_CR15","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1038\/nature11319","volume":"488","author":"MJ Claesson","year":"2012","unstructured":"Claesson MJ, Jeffery IB, Conde S, Power SE, O\u2019connor EM, Cusack S, Harris HMB, Coakley M, Lakshminarayanan B, O\u2019Sullivan O, et al. Gut microbiota composition correlates with diet and health in the elderly. Nature. 2012; 488:178\u201384.","journal-title":"Nature"},{"key":"226_CR16","doi-asserted-by":"publisher","first-page":"e1005361","DOI":"10.1371\/journal.pcbi.1005361","volume":"13","author":"JC Claussen","year":"2017","unstructured":"Claussen JC, Skiecevi\u010dien\u0117 J, Wang J, Rausch P, Karlsen TH, Lieb W, Baines JF, Franke A, H\u00fctt MT. Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. PLoS Comput Biol. 2017; 13:e1005361.","journal-title":"PLoS Comput Biol"},{"key":"226_CR17","doi-asserted-by":"crossref","unstructured":"Friedman J, Alm E. Inferring correlation networks from genomic survey data. PLoS Comput Biol; 8:e1002687.","DOI":"10.1371\/journal.pcbi.1002687"},{"key":"226_CR18","doi-asserted-by":"publisher","DOI":"10.5962\/bhl.title.4489","volume-title":"The Struggle for Existence","author":"GF Gause","year":"1934","unstructured":"Gause GF. The Struggle for Existence. Baltimore: Williams & Wilkins; 1934."},{"issue":"3","key":"226_CR19","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.cels.2019.06.008","volume":"9","author":"RH Hsu","year":"2019","unstructured":"Hsu RH, Clark RL, Tan JW, Ahn JC, Gupta S, Romero PA, Venturelli OS. Microbial interaction network inference in microfluidic droplets. Cell Syst. 2019; 9(3):229\u201342. https:\/\/doi.org\/10.1016\/j.cels.2019.06.008.","journal-title":"Cell Syst"},{"key":"226_CR20","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1038\/ismej.2011.119","volume":"6","author":"A Barberan","year":"2012","unstructured":"Barberan A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012; 6:343\u201351.","journal-title":"ISME J"},{"key":"226_CR21","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3389\/fmicb.2014.00219","volume":"5","author":"D Berry","year":"2014","unstructured":"Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 2014; 5:219. https:\/\/doi.org\/10.3389\/fmicb.2014.00219.","journal-title":"Front Microbiol"},{"key":"226_CR22","doi-asserted-by":"crossref","unstructured":"Biswas S, McDonald M, Lundberg DS, Dangl JL, Jojic V. Learning microbial interaction networks from metagenomic count data. In: International Conference on Research in Computational Molecular Biology: 2015. p. 32\u201343.","DOI":"10.1007\/978-3-319-16706-0_6"},{"issue":"10","key":"226_CR23","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/nrg3552","volume":"14","author":"K Mitra","year":"2013","unstructured":"Mitra K, Carvunis AR, Ramesh SK, Ideker T. Integrative approaches for finding modular structure in biological networks. Nat Rev Genet. 2013; 14(10):719\u201332. https:\/\/doi.org\/10.1038\/nrg3552.","journal-title":"Nat Rev Genet"},{"issue":"11","key":"226_CR24","doi-asserted-by":"publisher","first-page":"e0187822","DOI":"10.1371\/journal.pone.0187822","volume":"12","author":"I Chen","year":"2017","unstructured":"Chen I, Kelkar YD, Gu Y, Zhou J, Qiu X, Wu H. High-dimensional linear state space models for dynamic microbial interaction networks. PloS ONE. 2017; 12(11):e0187822.","journal-title":"PloS ONE"},{"issue":"1","key":"226_CR25","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1073\/pnas.1311322111","volume":"111","author":"S Marino","year":"2014","unstructured":"Marino S, Baxter NT, Huffnagle GB, Petrosino JF, Schloss PD. Mathematical modeling of primary succession of murine intestinal microbiota. Proc Natl Acad Sci. 2014; 111(1):439\u201344.","journal-title":"Proc Natl Acad Sci"},{"issue":"6","key":"226_CR26","doi-asserted-by":"publisher","first-page":"402","DOI":"10.2174\/138920209789177575","volume":"10","author":"BJ Yoon","year":"2009","unstructured":"Yoon BJ. Hidden Markov models and their applications in biological sequence analysis. Curr Genomics. 2009; 10(6):402\u201315. https:\/\/doi.org\/10.2174\/138920209789177575.","journal-title":"Curr Genomics"},{"key":"226_CR27","unstructured":"Durbin J, Koopman SJ. Time Series Analysis by State Space Methods: Second Edition, 2nd Revised ed.: Oxford Statistical Science Series; 2009."},{"issue":"132","key":"226_CR28","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1126\/scitranslmed.3003605","volume":"4","author":"P Gajer","year":"2012","unstructured":"Gajer P, Brotman RM, Bai G, Sakamoto J, Schutte UM, Zhong X, Koenig SSK, Fu L, Ma ZS, Zhou X, et al. Temporal dynamics of the human vaginal microbiota. Sci Transl Med. 2012; 4(132):132\u201352. https:\/\/doi.org\/10.1126\/scitranslmed.3003605PMID:22553250.","journal-title":"Sci Transl Med"},{"key":"226_CR29","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3389\/fped.2016.00057","volume":"4","author":"V Sagheddu","year":"2016","unstructured":"Sagheddu V, Patrone V, Miragoli F, Puglisi E, Morelli L. Infant early gut colonization by Lachnospiraceae: high frequency of Ruminococcus gnavus. Front Pediatr. 2016; 4:57. https:\/\/doi.org\/10.3389\/fped.2016.00057.","journal-title":"Front Pediatr"},{"key":"226_CR30","doi-asserted-by":"publisher","first-page":"2420","DOI":"10.1038\/ajg.2010.281","volume":"105","author":"CW Png","year":"2010","unstructured":"Png CW, Lind\u00e9n SK, Gilshenan KS, Zoetendal EG, McSweeney CS, Sly LI, McGuckin MA, Florin THJ. Mucolytic bacteria with increased prevalence in IBD mucosa augment in vitro utilization of mucin by other bacteria. Am J Gastroenterol. 2010; 105:2420\u20138. https:\/\/doi.org\/10.1038\/ajg.2010.281.","journal-title":"Am J Gastroenterol"},{"issue":"4","key":"226_CR31","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/S1499-3872(17)60019-5","volume":"16","author":"F Shen","year":"2017","unstructured":"Shen F, Zheng RD, Sun XQ, Ding WJ, Wang XY, Fan JG. Gut microbiota dysbiosis in patients with non-alcoholic fatty liver disease. Hepatobiliary Pancreat Dis Int. 2017; 16(4):375\u201381. https:\/\/doi.org\/10.1016\/S1499-3872(17)60019-5. PMID: 28823367.","journal-title":"Hepatobiliary Pancreat Dis Int"},{"key":"226_CR32","doi-asserted-by":"crossref","unstructured":"Potts RB. Some generalized order-disorder transformations. In: Mathematical Proceedings of the Cambridge Philosophical Society: 1952. p. 106\u20139, Cambridge University Press.","DOI":"10.1017\/S0305004100027419"},{"key":"226_CR33","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1214\/09-AOS691","volume":"38","author":"P Ravikumar","year":"2010","unstructured":"Ravikumar P, Wainwright MJ, Lafferty JD. High-dimensional Ising model selection using L1 regularized logistic regression. Ann Stat. 2010; 38:1287\u2013319.","journal-title":"Ann Stat"},{"key":"226_CR34","doi-asserted-by":"crossref","unstructured":"Wainwright MJ, Jordan MI. Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends\u00ae in Machine Learning. 2008; 1(1\u01702):1\u2013305. doi:10.1561\/2200000001.","DOI":"10.1561\/2200000001"},{"key":"226_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-349-15634-4","volume-title":"An introduction to multivariate techniques for social and behavioural sciences","author":"S Bennett","year":"1976","unstructured":"Bennett S. An introduction to multivariate techniques for social and behavioural sciences. New York: Wiley; 1976."},{"key":"226_CR36","doi-asserted-by":"publisher","DOI":"10.1201\/EBK0824740993","volume-title":"Dynamic programming: Foundations and principles","author":"M Sniedovich","year":"2010","unstructured":"Sniedovich M. Dynamic programming: Foundations and principles. New York: Taylor & Francis; 2010. ISBN 978-0-8247-4099-3."},{"issue":"6","key":"226_CR37","first-page":"1","volume":"32","author":"B Tatiana","year":"2009","unstructured":"Tatiana B, Didier C, David RH, Derek Y. mixtools: An R Package for analyzing finite mixture models. J Stat Softw. 2009; 32(6):1\u201329.","journal-title":"J Stat Softw"},{"key":"226_CR38","unstructured":"Weihs L, Plummer M. Computing the singular BIC for multiple models. 2016. https:\/\/cran.rproject.org\/web\/packages\/sBIC. R package, version 0.2.0."},{"key":"226_CR39","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1111\/j.1467-9868.2007.00627.x","volume":"70","author":"L Meier","year":"2008","unstructured":"Meier L, Geer S, Buhlmann P. The group lasso for logistic regression. J R Stat Soc Ser B Stat Methodol. 2008; 70:53\u201371.","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"1","key":"226_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010; 33(1):1.","journal-title":"J Stat Softw"},{"key":"226_CR41","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/10618600.1998.10474784","volume":"7","author":"WJ Fu","year":"1998","unstructured":"Fu WJ. Penalized regressions: the bridge versus the lasso. J Comput Graph Stat. 1998; 7:397\u2013416.","journal-title":"J Comput Graph Stat"},{"key":"226_CR42","first-page":"555","volume":"22","author":"J Chen","year":"2012","unstructured":"Chen J, Chen Z. Extended BIC for small-n-large-P sparse GLM. Stat Sin. 2012; 22:555\u201374.","journal-title":"Stat Sin"},{"issue":"3","key":"226_CR43","doi-asserted-by":"publisher","first-page":"1436?","DOI":"10.1214\/009053606000000281","volume":"34","author":"N Meinshansen","year":"2006","unstructured":"Meinshansen N, Buhlmann P. High dimensional graphs and variable selection with lasso. Ann Stat. 2006; 34(3):1436?-62.","journal-title":"Ann Stat"},{"key":"226_CR44","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1111\/biom.12202","volume":"70","author":"J Cheng","year":"2014","unstructured":"Cheng J, Levina E, Wang P, Zhu J. A sparse Ising model with covariates. Biometrics. 2014; 70:943\u201353.","journal-title":"Biometrics"},{"key":"226_CR45","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","volume":"9","author":"J Friedman","year":"2008","unstructured":"Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistcs. 2008; 9:432\u201341.","journal-title":"Biostatistcs"},{"key":"226_CR46","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1038\/nmeth.3869","volume":"13","author":"BJ Callahan","year":"2016","unstructured":"Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016; 13:581\u20133. https:\/\/doi.org\/10.1038\/nmeth.3869.","journal-title":"Nat Methods"},{"key":"226_CR47","doi-asserted-by":"publisher","first-page":"7624","DOI":"10.1038\/ncomms8624","volume":"6","author":"LE Tailford","year":"2015","unstructured":"Tailford LE, Owen CD, Walshaw J, Crost EH, Hardy-Goddard J, Le Gall G, et al. Discovery of intramolecular trans-sialidases in human gut microbiota suggests novel mechanisms of mucosal adaptation. Nat Commun. 2015; 6:7624. https:\/\/doi.org\/10.1038\/ncomms8624.","journal-title":"Nat Commun"},{"key":"226_CR48","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1038\/nchembio.1864","volume":"11","author":"AS Devlin","year":"2015","unstructured":"Devlin AS, Fischbach MA. A biosynthetic pathway for a prominent class of microbiota-derived bile acids. Nat Chem Biol. 2015; 11:685\u201390. https:\/\/doi.org\/10.1038\/nchembio.1864.","journal-title":"Nat Chem Biol"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-020-00226-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-020-00226-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-020-00226-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T12:36:48Z","timestamp":1723725408000},"score":1,"resource":{"primary":{"URL":"https:\/\/biodatamining.biomedcentral.com\/articles\/10.1186\/s13040-020-00226-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,7]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["226"],"URL":"https:\/\/doi.org\/10.1186\/s13040-020-00226-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.06.02.130914","asserted-by":"object"}]},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,7]]},"assertion":[{"value":"2 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16"}}