{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T22:38:11Z","timestamp":1783204691194,"version":"3.54.6"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["U19AI106772"],"award-info":[{"award-number":["U19AI106772"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011661","name":"Pacific Northwest National Laboratory","doi-asserted-by":"publisher","award":["73639"],"award-info":[{"award-number":["73639"]}],"id":[{"id":"10.13039\/100011661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04197-2","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T09:03:11Z","timestamp":1625562191000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Hypergraph models of biological networks to identify genes critical to pathogenic viral response"],"prefix":"10.1186","volume":"22","author":[{"given":"Song","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Heath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brett","family":"Jefferson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cliff","family":"Joslyn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry","family":"Kvinge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugh D.","family":"Mitchell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brenda","family":"Praggastis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amie J.","family":"Eisfeld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy C.","family":"Sims","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larissa B.","family":"Thackray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shufang","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin B.","family":"Walters","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J.","family":"Halfmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle","family":"Westhoff-Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vineet D.","family":"Menachery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy P.","family":"Sheahan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam S.","family":"Cockrell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob F.","family":"Kocher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelly G.","family":"Stratton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalie C.","family":"Heller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa M.","family":"Bramer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael S.","family":"Diamond","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralph S.","family":"Baric","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katrina M.","family":"Waters","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshihiro","family":"Kawaoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason E.","family":"McDermott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2069-5594","authenticated-orcid":false,"given":"Emilie","family":"Purvine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"issue":"1","key":"4197_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1186\/s12918-016-0336-6","volume":"10","author":"JE McDermott","year":"2016","unstructured":"McDermott JE, Mitchell HD, Gralinski LE, Eisfeld AJ, Josset L, Bankhead A, Neumann G, Tilton SC, Sch\u00e4fer A, Li C, et al. The effect of inhibition of PP1 and TNF\u03b1 signaling on pathogenesis of SARS coronavirus. BMC Syst Biol. 2016;10(1):93. https:\/\/doi.org\/10.1186\/s12918-016-0336-6.","journal-title":"BMC Syst Biol"},{"key":"4197_CR2","doi-asserted-by":"publisher","first-page":"200","DOI":"10.3389\/fcell.2019.00200","volume":"7","author":"HD Mitchell","year":"2019","unstructured":"Mitchell HD, Eisfeld AJ, Stratton KG, Heller NC, Bramer LM, Wen J, McDermott JE, Gralinski LE, Sims AC, Le MQ, Baric RS, Kawaoka Y, Waters KM. The role of EGFR in influenza pathogenicity: multiple network-based approaches to identify a key regulator of non-lethal infections. Front Cell Dev Biol. 2019;7:200. https:\/\/doi.org\/10.3389\/fcell.2019.00200.","journal-title":"Front Cell Dev Biol"},{"issue":"9","key":"4197_CR3","doi-asserted-by":"publisher","first-page":"2649","DOI":"10.1093\/bioinformatics\/btaa008","volume":"36","author":"VD Tran","year":"2020","unstructured":"Tran VD, Sperduti A, Backofen R, Costa F. Heterogeneous networks integration for disease gene prioritization with node kernels. Bioinformatics. 2020;36(9):2649\u201356. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa008.","journal-title":"Bioinformatics"},{"issue":"3","key":"4197_CR4","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1039\/B708489G","volume":"4","author":"A Adourian","year":"2008","unstructured":"Adourian A, Jennings E, Balasubramanian R, Hines WM, Damian D, Plasterer TN, Clish CB, Stroobant P, McBurney R, Verheij ER, Bobeldijk I, van der Greef J, Lindberg J, Kenne K, Andersson U, Hellmold H, Nilsson K, Salter H, Schuppe-Koistinen I. Correlation network analysis for data integration and biomarker selection. Mol Biosyst. 2008;4(3):249\u201359. https:\/\/doi.org\/10.1039\/B708489G.","journal-title":"Mol Biosyst"},{"issue":"1","key":"4197_CR5","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1371\/journal.ppat.1000719","volume":"6","author":"DL Diamond","year":"2010","unstructured":"Diamond DL, Syder AJ, Jacobs JM, Sorensen CM, Walters KA, Proll SC, McDermott JE, Gritsenko MA, Zhang Q, Zhao R, Metz TO, Camp DG, Waters KM, Smith RD, Rice CM, Katze MG. Temporal proteome and lipidome profiles reveal hepatitis C virus-associated reprogramming of hepatocellular metabolism and bioenergetics. PLoS Pathog. 2010;6(1):56. https:\/\/doi.org\/10.1371\/journal.ppat.1000719.","journal-title":"PLoS Pathog"},{"key":"4197_CR6","doi-asserted-by":"publisher","DOI":"10.1128\/mBio.01343-17","author":"TV Maier","year":"2017","unstructured":"Maier TV, Lucio M, Lee LH, VerBerkmoes NC, Brislawn CJ, Bernhardt J, Lamendella R, McDermott JE, Bergeron N, Heinzmann SS, Morton JT, Gonzalez A, Ackermann G, Knight R, Riedel K, Krauss RM, Schmitt-Kopplin P, Jansson JK. Impact of dietary resistant starch on the human gut microbiome, metaproteome, and metabolome. MBio. 2017. https:\/\/doi.org\/10.1128\/mBio.01343-17.","journal-title":"MBio"},{"key":"4197_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007241","author":"RS McClure","year":"2019","unstructured":"McClure RS, Wendler JP, Adkins JN, Swanstrom J, Baric R, Kaiser BLD, Oxford KL, Waters KM, McDermott JE. Unified feature association networks through integration of transcriptomic and proteomic data. PLoS Comput Biol. 2019. https:\/\/doi.org\/10.1371\/journal.pcbi.1007241.","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"4197_CR8","doi-asserted-by":"publisher","first-page":"4463","DOI":"10.1038\/s41467-019-12474-1","volume":"10","author":"M Lempp","year":"2019","unstructured":"Lempp M, Farke N, Kuntz M, Freibert SA, Lill R, Link H. Systematic identification of metabolites controlling gene expression in E. coli. Nat Commun. 2019;10(1):4463. https:\/\/doi.org\/10.1038\/s41467-019-12474-1.","journal-title":"Nat Commun"},{"key":"4197_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002722","author":"JE McDermott","year":"2012","unstructured":"McDermott JE, Jarman K, Taylor R, Lancaster M, Shankaran H, Vartanian KB, Stevens SL, Stenzel-Poore MP, Sanfilippo A. Modeling dynamic regulatory processes in stroke. PLoS Comput Biol. 2012. https:\/\/doi.org\/10.1371\/journal.pcbi.1002722.","journal-title":"PLoS Comput Biol"},{"issue":"8","key":"4197_CR10","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.1039\/c1mb05006k","volume":"7","author":"JE McDermott","year":"2011","unstructured":"McDermott JE, Oehmen CS, McCue LA, Hill E, Choi DM, Stockel J, Liberton M, Pakrasi HB, Sherman LA. A model of cyclic transcriptomic behavior in the cyanobacterium Cyanothece sp. ATCC 51142. Mol Biosyst. 2011;7(8):2407\u201318. https:\/\/doi.org\/10.1039\/c1mb05006k.","journal-title":"Mol Biosyst"},{"key":"4197_CR11","volume-title":"Network science","author":"A-L Barab\u00e1si","year":"2016","unstructured":"Barab\u00e1si A-L. Network science. UK: Cambridge University Press; 2016."},{"key":"4197_CR12","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1038\/s41467-019-10431-6","volume":"10","author":"I Iacopini","year":"2019","unstructured":"Iacopini I, Petri G, Barrat A, Latora V. Simplicial models of social contagion. Nat Commun. 2019;10:2485. https:\/\/doi.org\/10.1038\/s41467-019-10431-6.","journal-title":"Nat Commun"},{"issue":"5","key":"4197_CR13","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1371\/journal.pcbi.1000385","volume":"5","author":"S Klamt","year":"2009","unstructured":"Klamt S, Haus U-U, Theis F. Hypergraphs and cellular networks. PLoS Comput Biol. 2009;5(5):56. https:\/\/doi.org\/10.1371\/journal.pcbi.1000385.","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"4197_CR14","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1140\/epjds\/s13688-017-0114-8","volume":"6","author":"A Patania","year":"2017","unstructured":"Patania A, Petri G, Vaccarino F. The shape of collaborations. EPJ Data Sci. 2017;6(1):18. https:\/\/doi.org\/10.1140\/epjds\/s13688-017-0114-8.","journal-title":"EPJ Data Sci"},{"key":"4197_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-020-09701-7","author":"MA Javidian","year":"2020","unstructured":"Javidian MA, Wang Z, Lu L, Valtorta M. On a hypergraph probabilistic graphical model. Ann Math Artif Intell. 2020. https:\/\/doi.org\/10.1007\/s10472-020-09701-7.","journal-title":"Ann Math Artif Intell"},{"key":"4197_CR16","unstructured":"Joslyn CA, Aksoy S, Callahan TJ, Hunter L, Jefferson B, Praggastis B, Purvine EA, Tripodi IJ. Hypernetwork science: from multidimensional networks to computational topology. In: International conference on complex systems (ICCS 2020). 2020. https:\/\/arxiv.org\/abs\/2003.11782."},{"key":"4197_CR17","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2018.0581","author":"W Leal","year":"2019","unstructured":"Leal W, Restrepo G. Formal structure of periodic system of elements. Proc R Soc A. 2019. https:\/\/doi.org\/10.1098\/rspa.2018.0581.","journal-title":"Proc R Soc A"},{"key":"4197_CR18","doi-asserted-by":"publisher","unstructured":"Minas M. Hypergraphs as a uniform diagram representation model. In: Proceedings of the 6th international workshop on theory and applications of graph transformations. Berlin: Springer; 1998. p. 281\u201395. https:\/\/doi.org\/10.1007\/978-3-540-46464-8_20.","DOI":"10.1007\/978-3-540-46464-8_20"},{"key":"4197_CR19","unstructured":"Chitra U. Random walks on hypergraphs with applications to disease-gene prioritization. PhD thesis, Brown University. 2017."},{"key":"4197_CR20","unstructured":"Tran L. Hypergraph and protein function prediction with gene expression data. 2012. arXiv preprint arXiv:1212.0388."},{"key":"4197_CR21","doi-asserted-by":"publisher","unstructured":"Ramadan E, Tarafdar A, Pothen A. A hypergraph model for the yeast protein complex network. In: 18th International parallel and distributed processing symposium. 2004. p. 189. https:\/\/doi.org\/10.1109\/IPDPS.2004.1303205.","DOI":"10.1109\/IPDPS.2004.1303205"},{"key":"4197_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-12-132","author":"W Zhou","year":"2011","unstructured":"Zhou W, Nakhleh L. Properties of metabolic graphs: biological organization or representation artifacts? BMC Bioinf. 2011. https:\/\/doi.org\/10.1186\/1471-2105-12-132.","journal-title":"BMC Bioinf"},{"key":"4197_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/nrmicro2419","author":"R De Smet","year":"2010","unstructured":"De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Microbiol. 2010. https:\/\/doi.org\/10.1038\/nrmicro2419.","journal-title":"Nat Rev Microbiol"},{"issue":"6","key":"4197_CR24","doi-asserted-by":"publisher","first-page":"36465","DOI":"10.1371\/journal.pone.0036465","volume":"7","author":"JE McDermott","year":"2012","unstructured":"McDermott JE, Vartanian KB, Mitchell H, Stevens SL, Sanfilippo A, Stenzel-Poore MP. Identification and validation of ifit1 as an important innate immune bottleneck. PLoS ONE. 2012;7(6):36465.","journal-title":"PLoS ONE"},{"issue":"3","key":"4197_CR25","doi-asserted-by":"publisher","first-page":"01174","DOI":"10.1128\/mBio.01174-14","volume":"5","author":"VD Menachery","year":"2014","unstructured":"Menachery VD, Eisfeld AJ, Schafer A, Josset L, Sims AC, Proll S, Fan S, Li C, Neumann G, Tilton SC, Chang J, Gralinski LE, Long C, Green R, Williams CM, Weiss J, Matzke MM, Webb-Robertson BJ, Schepmoes AA, Shukla AK, Metz TO, Smith RD, Waters KM, Katze MG, Kawaoka Y, Baric RS. Pathogenic influenza viruses and coronaviruses utilize similar and contrasting approaches to control interferon-stimulated gene responses. MBio. 2014;5(3):01174\u201314. https:\/\/doi.org\/10.1128\/mBio.01174-14.","journal-title":"MBio"},{"issue":"6","key":"4197_CR26","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1042\/BSR20180250","volume":"38","author":"A Basters","year":"2018","unstructured":"Basters A, Knobeloch K-P, Fritz G. Usp18-a multifunctional component in the interferon response. Biosci Rep. 2018;38(6):56.","journal-title":"Biosci Rep"},{"key":"4197_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0069374","author":"HD Mitchell","year":"2013","unstructured":"Mitchell HD, Eisfeld AJ, Sims AC, McDermott JE, Matzke MM, Webb-Robertson B-JM, Tilton SC, Tchitchek N, Josset L, Li C, et al. A network integration approach to predict conserved regulators related to pathogenicity of influenza and sars-cov respiratory viruses. PLoS ONE. 2013. https:\/\/doi.org\/10.1371\/journal.pone.0069374.","journal-title":"PLoS ONE"},{"issue":"1","key":"4197_CR28","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1371\/journal.pbio.0050008","volume":"5","author":"JJ Faith","year":"2007","unstructured":"Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5(1):56. https:\/\/doi.org\/10.1371\/journal.pbio.0050008.","journal-title":"PLoS Biol"},{"key":"4197_CR29","unstructured":"Hagberg A, Swart P, Chult DS. Exploring network structure, dynamics, and function using NetworkX. Technical report, Los Alamos National Lab.(LANL), Los Alamos. 2008."},{"issue":"2","key":"4197_CR30","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1371\/journal.ppat.0040032","volume":"4","author":"MD Dyer","year":"2008","unstructured":"Dyer MD, Murali TM, Sobral BW. The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog. 2008;4(2):56. https:\/\/doi.org\/10.1371\/journal.ppat.0040032.","journal-title":"PLoS Pathog"},{"issue":"43","key":"4197_CR31","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545\u201350. https:\/\/doi.org\/10.1073\/pnas.0506580102.","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"4197_CR32","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1186\/s12859-019-2897-z","volume":"20","author":"EY Kim","year":"2019","unstructured":"Kim EY, Ashlock D, Yoon SH. Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks. BMC Bioinf. 2019;20(1):328. https:\/\/doi.org\/10.1186\/s12859-019-2897-z.","journal-title":"BMC Bioinf"},{"key":"4197_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-06247-5","author":"W Mi","year":"2018","unstructured":"Mi W, Zhang Y, Lyu J, Wang X, Tong Q, Peng D, Xue Y, Tencer AH, Wen H, Li W, et al. The ZZ-type zinc finger of ZZZ3 modulates the ATAC complex-mediated histone acetylation and gene activation. Nat Commun. 2018. https:\/\/doi.org\/10.1038\/s41467-018-06247-5.","journal-title":"Nat Commun"},{"issue":"21","key":"4197_CR34","doi-asserted-by":"publisher","first-page":"10955","DOI":"10.1128\/JVI.05792-11","volume":"85","author":"C Li","year":"2011","unstructured":"Li C, Bankhead A, Eisfeld AJ, Hatta Y, Jeng S, Chang JH, Aicher LD, Proll S, Ellis AL, Law GL, et al. Host regulatory network response to infection with highly pathogenic h5n1 avian influenza virus. J Virol. 2011;85(21):10955\u201367.","journal-title":"J Virol"},{"key":"4197_CR35","unstructured":"Berge C. Hypergraphs: combinatorics of finite sets, vol. 45. Elsevier; 1984."},{"issue":"1","key":"4197_CR36","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1140\/epjds\/s13688-020-00231-0","volume":"9","author":"SG Aksoy","year":"2020","unstructured":"Aksoy SG, Joslyn C, Marrero CO, Praggastis B, Purvine E. Hypernetwork science via high-order hypergraph walks. EPJ Data Sci. 2020;9(1):16. https:\/\/doi.org\/10.1140\/epjds\/s13688-020-00231-0.","journal-title":"EPJ Data Sci"},{"issue":"4","key":"4197_CR37","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1371\/journal.pcbi.0030059","volume":"3","author":"H Yu","year":"2007","unstructured":"Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol. 2007;3(4):59.","journal-title":"PLoS Comput Biol"},{"issue":"2","key":"4197_CR38","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1089\/cmb.2008.04TT","volume":"16","author":"JE McDermott","year":"2009","unstructured":"McDermott JE, Taylor RC, Yoon H, Heffron F. Bottlenecks and hubs in inferred networks are important for virulence in salmonella typhimurium. J Comput Biol. 2009;16(2):169\u201380.","journal-title":"J Comput Biol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04197-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-021-04197-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04197-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T09:07:17Z","timestamp":1625562437000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04197-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,29]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["4197"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04197-2","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,29]]},"assertion":[{"value":"1 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Human lungs were obtained under protocol 03-1396, which was approved by the University of North Carolina at Chapel Hill Biomedical Institutional Review Board, and donors gave informed consent. \u2018Omics data collection studies for animal samples performed at UNC Chapel Hill were performed in animal biosafety level 3 facilities and were conducted under protocols approved by the Institutional Animal Care and Use Committee at UNC Chapel Hill (IACUC protocol #16-251) according to guidelines set by the Association for the Assessment and Accreditation of Laboratory Animal Care and the U.S. Department of Agriculture. All animal experiments and procedures performed at UW-Madison were approved by the UW-Madison School of Veterinary Medicine Animal Care and Use Committee under relevant institutional and American Veterinary Association guidelines. West Nile virus work in mice was carried out in strict accordance with the recommendations in the <i>Guide for the Care and Use of Laboratory Animals<\/i> of the National Institutes of Health. The protocols were approved by the Institutional Animal Care and Use Committee at the Washington University School of Medicine (Assurance number A3381-01).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"RB has ongoing unrelated collaborations and\/or sponsored research agreements with Moderna, VaxArt, Eli Lily, Pfizer, Takeda and Ridgeback Biosciences. MD is a consultant for Inbios, Vir Biotechnology, and Fortressa Biotech and on the Scientific Advisory Boards of Moderna and Immunome. The Diamond laboratory has received unrelated funding support in sponsored research agreements from Moderna, Vir Biotechnology, Kaleido, and Emergent BioSolutions.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"287"}}