{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T14:37:37Z","timestamp":1784385457493,"version":"3.55.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"American Heart Association,United States","award":["25PRE1409798"],"award-info":[{"award-number":["25PRE1409798"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The endothelial tube formation assay is an established in vitro model for evaluating angiogenesis. Although widely used, quantification of angiogenic behavior in such assays remains semi-empirical and often lacks spatial, topological, and structural context. Here, we present a graph-theoretic framework to quantify network morphology, temporal dynamics, and spatial heterogeneity in tube formation assays. We simulated two distinct angiogenic network morphologies using human umbilical vein endothelial cells (HUVECs) seeded at two densities and imaged at 2, 4, and 18\u00a0h post-seeding. Skeletonized images were converted to mathematical graphs from which 11 graph-based metrics were extracted. This framework captured both morphological differences and temporal progression. Sparse networks exhibited significantly higher <jats:italic>average node degree<\/jats:italic> (<jats:italic>p<\/jats:italic>\u2009=\u20090.00079), <jats:italic>clustering coefficient<\/jats:italic> (<jats:italic>p<\/jats:italic>\u2009=\u20090.00109), and <jats:italic>tortuosity<\/jats:italic> (<jats:italic>p<\/jats:italic>\u2009=\u20090.0171), whereas dense networks showed greater <jats:italic>node and edges counts<\/jats:italic> (<jats:italic>p<\/jats:italic>\u2009=\u20090.00109). Over time, networks evolved from fragmented forms at 2\u00a0h to integrated structures at 18\u00a0h, as reflected by increased <jats:italic>largest component size<\/jats:italic> (<jats:italic>p<\/jats:italic>\u2009=\u20090.00216), <jats:italic>connectivity index<\/jats:italic> (<jats:italic>p<\/jats:italic>\u2009=\u20090.00216), and <jats:italic>efficiency<\/jats:italic> (<jats:italic>p<\/jats:italic>\u2009=\u20090.0152). ROC AUC analysis revealed that metrics such as <jats:italic>average degree<\/jats:italic> (AUC\u2009=\u20090.98) and <jats:italic>clustering coefficient<\/jats:italic> (AUC\u2009=\u20090.96) effectively distinguished between sparse and dense morphologies, while component-based metrics perfectly separated 2- and 18-hour networks (AUC\u2009=\u20091.00). Radial zone analysis revealed that vascular distribution becomes more compartmentalized over time, with increasing standard deviation and coefficient of variation. This approach provides a sensitive and scalable method for quantifying angiogenic dynamics, offering insight into both therapeutic efficacy and disease-related vascular remodeling.<\/jats:p>","DOI":"10.1186\/s13040-025-00478-1","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T11:14:01Z","timestamp":1759403641000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A graph-theoretic framework for quantitative analysis of angiogenic networks"],"prefix":"10.1186","volume":"18","author":[{"given":"Goodluck","family":"Okoro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pawel","family":"Wityk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael B.","family":"Nelappana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl A.","family":"Jackiewicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronica Z.","family":"Kucharczyk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annie","family":"Tigranyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine C.","family":"Applegate","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iwona T.","family":"Dobrucki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence W.","family":"Dobrucki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,2]]},"reference":[{"key":"478_CR1","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1161\/ATVBAHA.120.312862","volume":"40","author":"JP Cooke","year":"2020","unstructured":"Cooke JP, Meng S. Vascular regeneration in peripheral artery disease. Arterioscler Thromb Vasc Biol. 2020;40:1627\u201334. https:\/\/doi.org\/10.1161\/ATVBAHA.120.312862.","journal-title":"Arterioscler Thromb Vasc Biol"},{"key":"478_CR2","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1161\/CIRCRESAHA.121.318266","volume":"128","author":"BH Annex","year":"2021","unstructured":"Annex BH, Cooke JP. New directions in therapeutic angiogenesis and arteriogenesis in peripheral arterial disease. Circ Res. 2021;128:1944\u201357. https:\/\/doi.org\/10.1161\/CIRCRESAHA.121.318266.","journal-title":"Circ Res"},{"key":"478_CR3","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s40364-025-00779-x","volume":"13","author":"X Liu","year":"2025","unstructured":"Liu X, Zhang J, Yi T, Li H, Tang X, Liu D, et al. Decoding tumor angiogenesis: pathways, mechanisms, and future directions in anti-cancer strategies. Biomark Res. 2025;13:62. https:\/\/doi.org\/10.1186\/s40364-025-00779-x.","journal-title":"Biomark Res"},{"key":"478_CR4","doi-asserted-by":"crossref","unstructured":"Kelley M, Fierstein S, Purkey L, DeCicco-Skinner K. Endothelial cell tube formation assay: an in vitro model for angiogenesis. 2022:187\u201396. https:\/\/pubmed.ncbi.nlm.nih.gov\/35451757\/.","DOI":"10.1007\/978-1-0716-2217-9_12"},{"key":"478_CR5","doi-asserted-by":"crossref","unstructured":"Guidolin D, Albertin G. Tube formation in vitro angiogenesis assay. 2012:281\u201393. https:\/\/doi.org\/10.1016\/B978-0-12-405914-6.00015-9.","DOI":"10.1016\/B978-0-12-405914-6.00015-9"},{"key":"478_CR6","doi-asserted-by":"publisher","first-page":"705","DOI":"10.4196\/kjpp.2018.22.6.705","volume":"22","author":"H Lee","year":"2018","unstructured":"Lee H, Kang K-T. Advanced tube formation assay using human endothelial colony forming cells for in vitro evaluation of angiogenesis. Korean J Physiol Pharmacol. 2018;22:705. https:\/\/doi.org\/10.4196\/kjpp.2018.22.6.705.","journal-title":"Korean J Physiol Pharmacol"},{"key":"478_CR7","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s10456-018-9652-3","volume":"22","author":"JA Montoya-Zegarra","year":"2019","unstructured":"Montoya-Zegarra JA, Russo E, Runge P, Jadhav M, Willrodt A-H, Stoma S, et al. AutoTube: a novel software for the automated morphometric analysis of vascular networks in tissues. Angiogenesis. 2019;22:223\u201336. https:\/\/doi.org\/10.1007\/s10456-018-9652-3.","journal-title":"Angiogenesis"},{"key":"478_CR8","doi-asserted-by":"publisher","first-page":"11568","DOI":"10.1038\/s41598-020-67289-8","volume":"10","author":"G Carpentier","year":"2020","unstructured":"Carpentier G, Berndt S, Ferratge S, Rasband W, Cuendet M, Uzan G, et al. Angiogenesis analyzer for ImageJ \u2014 A comparative morphometric analysis of endothelial tube formation assay and fibrin bead assay. Sci Rep. 2020;10:11568. https:\/\/doi.org\/10.1038\/s41598-020-67289-8.","journal-title":"Sci Rep"},{"key":"478_CR9","doi-asserted-by":"publisher","first-page":"188","DOI":"10.3390\/computation11100188","volume":"11","author":"K Erciyes","year":"2023","unstructured":"Erciyes K. Graph-Theoretical analysis of biological networks: A survey. Computation. 2023;11:188. https:\/\/doi.org\/10.3390\/computation11100188.","journal-title":"Computation"},{"key":"478_CR10","doi-asserted-by":"publisher","unstructured":"Koutrouli M, Karatzas E, Paez-Espino D, Pavlopoulos GA. A guide to conquer the biological network era using graph theory. Front Bioeng Biotechnol. 2020;8. https:\/\/doi.org\/10.3389\/fbioe.2020.00034.","DOI":"10.3389\/fbioe.2020.00034"},{"key":"478_CR11","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/1756-0381-4-10","volume":"4","author":"GA Pavlopoulos","year":"2011","unstructured":"Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, et al. Using graph theory to analyze biological networks. BioData Min. 2011;4:10. https:\/\/doi.org\/10.1186\/1756-0381-4-10.","journal-title":"BioData Min"},{"key":"478_CR12","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.mvr.2003.11.005","volume":"67","author":"EM Wahl","year":"2004","unstructured":"Wahl EM, Quintas LV, Lurie LL, Gargano ML. A graph theory analysis of renal glomerular microvascular networks. Microvasc Res. 2004;67:223\u201330. https:\/\/doi.org\/10.1016\/j.mvr.2003.11.005.","journal-title":"Microvasc Res"},{"key":"478_CR13","doi-asserted-by":"publisher","first-page":"171592","DOI":"10.1098\/rsos.171592","volume":"5","author":"AP Alves","year":"2018","unstructured":"Alves AP, Mesquita ON, G\u00f3mez-Garde\u00f1es J, Agero U. Graph analysis of cell clusters forming vascular networks. R Soc Open Sci. 2018;5:171592. https:\/\/doi.org\/10.1098\/rsos.171592.","journal-title":"R Soc Open Sci"},{"key":"478_CR14","doi-asserted-by":"publisher","unstructured":"Stolz BJ, Kaeppler J, Markelc B, Braun F, Lipsmeier F, Muschel RJ, et al. Multiscale topology characterizes dynamic tumor vascular networks. Sci Adv. 2022;8. https:\/\/doi.org\/10.1126\/sciadv.abm2456.","DOI":"10.1126\/sciadv.abm2456"},{"key":"478_CR15","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s13278-018-0492-3","volume":"8","author":"A Strang","year":"2018","unstructured":"Strang A, Haynes O, Cahill ND, Narayan DA. Generalized relationships between characteristic path length, efficiency, clustering coefficients, and density. Soc Netw Anal Min. 2018;8:14. https:\/\/doi.org\/10.1007\/s13278-018-0492-3.","journal-title":"Soc Netw Anal Min"},{"key":"478_CR16","unstructured":"Rasband WS. (1997\u20132018). ImageJ. U.S. National Institutes of Health, Bethesda, Maryland, USA. Available at: https:\/\/imagej.nih.gov\/ij.nd."},{"key":"478_CR17","doi-asserted-by":"publisher","unstructured":"Hagberg AA, Schult DA, Swart PJ. Exploring network structure, dynamics, and function using networkX, 2008, pp. 11\u20135. https:\/\/doi.org\/10.25080\/TCWV9851","DOI":"10.25080\/TCWV9851"},{"key":"478_CR18","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with numpy. Nature. 2020;585:357\u201362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2.","journal-title":"Nature"},{"key":"478_CR19","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in python. Nat Methods. 2020;17:261\u201372. https:\/\/doi.org\/10.1038\/s41592-019-0686-2.","journal-title":"Nat Methods"},{"key":"478_CR20","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD, Matplotlib. A 2D graphics environment. Comput Sci Eng. 2007;9:90\u20135. https:\/\/doi.org\/10.1109\/MCSE.2007.55.","journal-title":"Comput Sci Eng"},{"key":"478_CR21","doi-asserted-by":"publisher","first-page":"e453","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt S, Sch\u00f6nberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, et al. scikit-image: image process python PeerJ. 2014;2:e453. https:\/\/doi.org\/10.7717\/peerj.453.","journal-title":"scikit-image: Image Process Python PeerJ"},{"key":"478_CR22","doi-asserted-by":"publisher","first-page":"895267","DOI":"10.1155\/2015\/895267","volume":"2015","author":"T Mapayi","year":"2015","unstructured":"Mapayi T, Viriri S, Tapamo J-R. Comparative study of retinal vessel segmentation based on global thresholding techniques. Comput Math Methods Med. 2015;2015:895267. https:\/\/doi.org\/10.1155\/2015\/895267.","journal-title":"Comput Math Methods Med"},{"key":"478_CR23","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.jvcir.2016.10.013","volume":"41","author":"C Sha","year":"2016","unstructured":"Sha C, Hou J, Cui H. A robust 2D otsu\u2019s thresholding method in image segmentation. J Vis Commun Image Represent. 2016;41:339\u201351. https:\/\/doi.org\/10.1016\/j.jvcir.2016.10.013.","journal-title":"J Vis Commun Image Represent"},{"key":"478_CR24","doi-asserted-by":"publisher","first-page":"21777","DOI":"10.37622\/IJAER\/10.9.2015.21777-21783","volume":"10","author":"SL Bangare","year":"2015","unstructured":"Bangare SL, Dubal A, Bangare PS, Patil ST. Reviewing otsu\u2019s method for image thresholding. Int J Appl Eng Res. 2015;10:21777\u201383. https:\/\/doi.org\/10.37622\/IJAER\/10.9.2015.21777-21783.","journal-title":"Int J Appl Eng Res"},{"key":"478_CR25","doi-asserted-by":"publisher","first-page":"7714","DOI":"10.3390\/ijms24097714","volume":"24","author":"V Ramakrishnan","year":"2023","unstructured":"Ramakrishnan V, Sch\u00f6nmehl R, Artinger A, Winter L, B\u00f6ck H, Schreml S, et al. 3D visualization, skeletonization and branching analysis of blood vessels in angiogenesis. Int J Mol Sci. 2023;24:7714. https:\/\/doi.org\/10.3390\/ijms24097714.","journal-title":"Int J Mol Sci"},{"key":"478_CR26","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1038\/s42003-021-02632-x","volume":"4","author":"A Fouladzadeh","year":"2021","unstructured":"Fouladzadeh A, Dorraki M, Min KKM, Cockshell MP, Thompson EJ, Verjans JW, et al. The development of tumour vascular networks. Commun Biol. 2021;4:1111. https:\/\/doi.org\/10.1038\/s42003-021-02632-x.","journal-title":"Commun Biol"},{"key":"478_CR27","doi-asserted-by":"crossref","unstructured":"Node Degree and Strength. Fundamentals of brain network analysis, Elsevier; 2016:115\u201336. https:\/\/doi.org\/10.1016\/B978-0-12-407908-3.00004-2.","DOI":"10.1016\/B978-0-12-407908-3.00004-2"},{"key":"478_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2014.07.001","volume":"544","author":"S Boccaletti","year":"2014","unstructured":"Boccaletti S, Bianconi G, Criado R, del Genio CI, G\u00f3mez-Garde\u00f1es J, Romance M, et al. The structure and dynamics of multilayer networks. Phys Rep. 2014;544:1\u2013122. https:\/\/doi.org\/10.1016\/j.physrep.2014.07.001.","journal-title":"Phys Rep"},{"key":"478_CR29","doi-asserted-by":"publisher","first-page":"198701","DOI":"10.1103\/PhysRevLett.87.198701","volume":"87","author":"V Latora","year":"2001","unstructured":"Latora V, Marchiori M. Efficient behavior of Small-World networks. Phys Rev Lett. 2001;87:198701. https:\/\/doi.org\/10.1103\/PhysRevLett.87.198701.","journal-title":"Phys Rev Lett"},{"key":"478_CR30","doi-asserted-by":"publisher","first-page":"115199","DOI":"10.1016\/j.chaos.2024.115199","volume":"186","author":"JU Legaria-Pe\u00f1a","year":"2024","unstructured":"Legaria-Pe\u00f1a JU, S\u00e1nchez-Morales F, Cort\u00e9s-Poza Y. Understanding post-angiogenic tumor growth: insights from vascular network properties in cellular automata modeling. Chaos Solitons Fractals. 2024;186:115199. https:\/\/doi.org\/10.1016\/j.chaos.2024.115199.","journal-title":"Chaos Solitons Fractals"},{"key":"478_CR31","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.mvr.2013.11.003","volume":"91","author":"S Lorthois","year":"2014","unstructured":"Lorthois S, Lauwers F, Cassot F. Tortuosity and other vessel attributes for arterioles and venules of the human cerebral cortex. Microvasc Res. 2014;91:99\u2013109. https:\/\/doi.org\/10.1016\/j.mvr.2013.11.003.","journal-title":"Microvasc Res"},{"key":"478_CR32","doi-asserted-by":"publisher","unstructured":"Wan Z, Xia X, Lo D, Murphy GC. How does machine learning change software development practices?? IEEE Trans Software Eng 2020:1\u20131. https:\/\/doi.org\/10.1109\/TSE.2019.2937083","DOI":"10.1109\/TSE.2019.2937083"},{"key":"478_CR33","doi-asserted-by":"publisher","first-page":"7279","DOI":"10.1038\/s41598-023-33090-6","volume":"13","author":"M Beter","year":"2023","unstructured":"Beter M, Abdollahzadeh A, Pulkkinen HH, Huang H, Orsenigo F, Magnusson PU, et al. SproutAngio: an open-source bioimage informatics tool for quantitative analysis of sprouting angiogenesis and lumen space. Sci Rep. 2023;13:7279. https:\/\/doi.org\/10.1038\/s41598-023-33090-6.","journal-title":"Sci Rep"},{"key":"478_CR34","doi-asserted-by":"publisher","unstructured":"Pereira M, Pinto J, Arteaga B, Guerra A, Jorge RN, Monteiro FJ, et al. A comprehensive look at in vitro angiogenesis image analysis software. Int J Mol Sci. 2023;24. https:\/\/doi.org\/10.3390\/ijms242417625.","DOI":"10.3390\/ijms242417625"},{"key":"478_CR35","doi-asserted-by":"publisher","first-page":"e27385","DOI":"10.1371\/journal.pone.0027385","volume":"6","author":"E Zudaire","year":"2011","unstructured":"Zudaire E, Gambardella L, Kurcz C, Vermeren S. A computational tool for quantitative analysis of vascular networks. PLoS ONE. 2011;6:e27385. https:\/\/doi.org\/10.1371\/journal.pone.0027385.","journal-title":"PLoS ONE"},{"key":"478_CR36","doi-asserted-by":"publisher","unstructured":"DeCicco-Skinner KL, Henry GH, Cataisson C, Tabib T, Gwilliam JC, Watson NJ, et al. Endothelial cell tube formation assay for the in vitro study of angiogenesis. J Visualized Experiments. 2014. https:\/\/doi.org\/10.3791\/51312.","DOI":"10.3791\/51312"},{"key":"478_CR37","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1083\/jcb.107.4.1589","volume":"107","author":"Y Kubota","year":"1988","unstructured":"Kubota Y, Kleinman HK, Martin GR, Lawley TJ. Role of laminin and basement membrane in the morphological differentiation of human endothelial cells into capillary-like structures. J Cell Biol. 1988;107:1589\u201398. https:\/\/doi.org\/10.1083\/jcb.107.4.1589.","journal-title":"J Cell Biol"},{"key":"478_CR38","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s10456-023-09876-7","volume":"26","author":"AC Dudley","year":"2023","unstructured":"Dudley AC, Griffioen AW. Pathological angiogenesis: mechanisms and therapeutic strategies. Angiogenesis. 2023;26:313\u201347. https:\/\/doi.org\/10.1007\/s10456-023-09876-7.","journal-title":"Angiogenesis"},{"key":"478_CR39","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1161\/CIRCRESAHA.108.188144","volume":"104","author":"B Larriv\u00e9e","year":"2009","unstructured":"Larriv\u00e9e B, Freitas C, Suchting S, Brunet I, Eichmann A. Guidance of vascular development. Circ Res. 2009;104:428\u201341. https:\/\/doi.org\/10.1161\/CIRCRESAHA.108.188144.","journal-title":"Circ Res"},{"key":"478_CR40","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1038\/nrm3176","volume":"12","author":"SP Herbert","year":"2011","unstructured":"Herbert SP, Stainier DYR. Molecular control of endothelial cell behaviour during blood vessel morphogenesis. Nat Rev Mol Cell Biol. 2011;12:551\u201364. https:\/\/doi.org\/10.1038\/nrm3176.","journal-title":"Nat Rev Mol Cell Biol"},{"key":"478_CR41","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1161\/01.HYP.0000184428.16429.be","volume":"46","author":"AR Pries","year":"2005","unstructured":"Pries AR, Reglin B, Secomb TW. Remodeling of blood vessels. Hypertension. 2005;46:725\u201331. https:\/\/doi.org\/10.1161\/01.HYP.0000184428.16429.be.","journal-title":"Hypertension"},{"key":"478_CR42","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1039\/c3ib40149a","volume":"5","author":"MB Chen","year":"2013","unstructured":"Chen MB, Whisler JA, Jeon JS, Kamm RD. Mechanisms of tumor cell extravasation in an in vitro microvascular network platform. Integr Biology. 2013;5:1262. https:\/\/doi.org\/10.1039\/c3ib40149a.","journal-title":"Integr Biology"},{"key":"478_CR43","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1038\/ncb3443","volume":"18","author":"G Costa","year":"2016","unstructured":"Costa G, Harrington KI, Lovegrove HE, Page DJ, Chakravartula S, Bentley K, et al. Asymmetric division coordinates collective cell migration in angiogenesis. Nat Cell Biol. 2016;18:1292\u2013301. https:\/\/doi.org\/10.1038\/ncb3443.","journal-title":"Nat Cell Biol"},{"key":"478_CR44","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1016\/j.ceb.2010.08.010","volume":"22","author":"HM Eilken","year":"2010","unstructured":"Eilken HM, Adams RH. Dynamics of endothelial cell behavior in sprouting angiogenesis. Curr Opin Cell Biol. 2010;22:617\u201325. https:\/\/doi.org\/10.1016\/j.ceb.2010.08.010.","journal-title":"Curr Opin Cell Biol"},{"key":"478_CR45","doi-asserted-by":"publisher","first-page":"2260","DOI":"10.1096\/fj.02-1041fje","volume":"17","author":"AH Zisch","year":"2003","unstructured":"Zisch AH, Lutolf MP, Ehrbar M, Raeber GP, Rizzi SC, Davies N, et al. Cell-demanded release of VEGF from synthetic, biointeractive cell\u2010ingrowth matrices for vascularized tissue growth. FASEB J. 2003;17:2260\u20132. https:\/\/doi.org\/10.1096\/fj.02-1041fje.","journal-title":"FASEB J"},{"key":"478_CR46","doi-asserted-by":"publisher","unstructured":"Merino-Gonz\u00e1lez C, Zu\u00f1iga FA, Escudero C, Ormazabal V, Reyes C, Nova-Lamperti E et al. Mesenchymal stem Cell-Derived extracellular vesicles promote angiogenesis: potencial clinical application. Front Physiol 2016;7. https:\/\/doi.org\/10.3389\/fphys.2016.00024.","DOI":"10.3389\/fphys.2016.00024"},{"key":"478_CR47","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.jdermsci.2017.11.008","volume":"89","author":"S Arndt","year":"2018","unstructured":"Arndt S, Unger P, Berneburg M, Bosserhoff A-K, Karrer S. Cold atmospheric plasma (CAP) activates angiogenesis-related molecules in skin keratinocytes, fibroblasts and endothelial cells and improves wound angiogenesis in an autocrine and paracrine mode. J Dermatol Sci. 2018;89:181\u201390. https:\/\/doi.org\/10.1016\/j.jdermsci.2017.11.008.","journal-title":"J Dermatol Sci"},{"key":"478_CR48","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1038\/s41592-019-0582-9","volume":"16","author":"S Berg","year":"2019","unstructured":"Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, et al. Ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16:1226\u201332. https:\/\/doi.org\/10.1038\/s41592-019-0582-9.","journal-title":"Nat Methods"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-025-00478-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-025-00478-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-025-00478-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T11:14:05Z","timestamp":1759403645000},"score":1,"resource":{"primary":{"URL":"https:\/\/biodatamining.biomedcentral.com\/articles\/10.1186\/s13040-025-00478-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,2]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["478"],"URL":"https:\/\/doi.org\/10.1186\/s13040-025-00478-1","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,2]]},"assertion":[{"value":"5 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","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":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"69"}}