{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:10:59Z","timestamp":1787314259455,"version":"build-2736575974"},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006601","name":"Fralin Life Science Institute, Virginia Polytechnic Institute and State University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["physoc.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Physiological Reports"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study aims to demonstrate that text phrase\u2010mining and natural language processing (NLP) can annotate huge quantities of obstetrics textual data for the discovery and evaluation of maternal protein\/gene (MPG)\u2010disease interactions involved in the preeclampsia pathway. We employ a phrase\u2010mining\/NLP pipeline to evaluate unique MPGs involved in six cardiovascular derangements with overlapping presentations during pregnancy. The diseases were matched with Medical Subject Headings. A textual corpus was developed from abstracts matched to these terms through PubMed. Fourty\u2010four MPGs were identified with respect to the diseases. Processing was performed, with unique scores for each MPG\u2010disease pair. Components of the score were calculated and weighted for distinctness, integrity, and popularity. Statistical analyses were conducted for the examination of protein\u2010disease relationships. Fourty\u2010four MPGs with known associations to cardiovascular disease and preeclampsia pathways were identified among the 6 diseases. MPGs shared across the greatest number of disease states were implicated in: (1) angiogenesis and vasoconstriction, (2) hemodynamic regulation, (3) hormonal regulation of metabolism, and (4) inflammation. NLP and text phrase\u2010mining are successfully applied to Obstetrics abstracts with accuracy and speed. This approach holds promise in synthesizing large volumes of data for presenting trends in the Obstetric literature and for the identification of promising biomarkers.<\/jats:p>","DOI":"10.14814\/phy2.70262","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:19:52Z","timestamp":1742343592000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Text phrase\u2010mining in identifying and classifying maternal proteins and genes across preeclampsia and similar pathologies"],"prefix":"10.14814","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2202-6284","authenticated-orcid":false,"given":"Jacqueline G.","family":"Urdang","sequence":"first","affiliation":[{"name":"Virginia Tech Carilion School of Medicine  Roanoke Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie","family":"Masters","sequence":"additional","affiliation":[{"name":"Carilion Clinic  Roanoke Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nneoma","family":"Edokobi","sequence":"additional","affiliation":[{"name":"Virginia Tech Carilion School of Medicine  Roanoke Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chitra","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"UC Davis  Davis California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnib","family":"Quazi","sequence":"additional","affiliation":[{"name":"UC Davis  Davis California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Liem","sequence":"additional","affiliation":[{"name":"UC Davis  Davis California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Ahrens","sequence":"additional","affiliation":[{"name":"Virginia Tech  Blacksburg Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Virginia Tech  Blacksburg Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan","family":"Whitham","sequence":"additional","affiliation":[{"name":"Carilion Clinic  Roanoke Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-1-0716-2305-3_2","article-title":"Text mining protocol to retrieve significant drug\u2010gene interactions from PubMed abstracts","volume":"2496","author":"Anand S.","year":"2022","journal-title":"Methods in Molecular Biology"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12917-021-03000-x"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/527S14a"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v19.i6.802"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm7117a1"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1161\/HYPERTENSIONAHA.107.107532"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2019.00337"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/cld.409"},{"issue":"2","key":"e_1_2_10_10_1","first-page":"64","article-title":"Immunohistochemical expression of myeloperoxidase in placental samples of systematic lupus erythematosus pregnancies","volume":"10","author":"Heidari Z.","year":"2016","journal-title":"Journal of Family & Reproductive Health"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.placenta.2012.01.004"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.medj.2023.07.005"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-023-06996-y"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1152\/ajpheart.00175.2018"},{"key":"e_1_2_10_15_1","unstructured":"National Institutes of Health. (2024).Mesh browser. U.S. National Library of Medicine.https:\/\/meshb.nlm.nih.gov\/treeView"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rpth.2024.102430"},{"issue":"9","key":"e_1_2_10_17_1","first-page":"1708","article-title":"IL\u20101\u03b2, IL\u201010 and TNF\u2010\u03b1 polymorphisms may affect systemic lupus erythematosus risk and phenotype","volume":"40","author":"Rzeszotarska E.","year":"2022","journal-title":"Clinical and Experimental Rheumatology"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamacardio.2023.4994"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0000000000004706"},{"issue":"3","key":"e_1_2_10_20_1","first-page":"74","article-title":"Multi\u2010dimensional, phrase\u2010based summarization in text cubes. IEEE data","volume":"39","author":"Tao F.","year":"2016","journal-title":"Engineering Bulletin"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1056\/EVIDoa2200161"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ekir.2024.05.016"},{"key":"e_1_2_10_23_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3390\/biom12091278","article-title":"Integrating text mining into the curation of disease maps","volume":"12","author":"Voskamp M.","year":"2022","journal-title":"Biomolecules"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1186\/s13104-023-06469-x","article-title":"Use of electronic health record data mining for heart failure subtyping","volume":"16","author":"Vuori M. A.","year":"2023","journal-title":"BMC Research Notes"},{"key":"e_1_2_10_25_1","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.bpobgyn.2011.02.007","article-title":"The genetics of pre\u2010eclampsia and other hypertensive disorders of pregnancy","volume":"4","author":"Williams P. J.","year":"2011","journal-title":"Best Practice & Research. Clinical Obstetrics & Gynaecology"},{"key":"e_1_2_10_26_1","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2022-071653","article-title":"Hypertensive disorders of pregnancy","volume":"381","author":"Wu P.","year":"2023","journal-title":"BMJ"}],"container-title":["Physiological Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/physoc.onlinelibrary.wiley.com\/doi\/pdf\/10.14814\/phy2.70262","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:18:27Z","timestamp":1743131907000},"score":1,"resource":{"primary":{"URL":"https:\/\/physoc.onlinelibrary.wiley.com\/doi\/10.14814\/phy2.70262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":25,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.14814\/phy2.70262"],"URL":"https:\/\/doi.org\/10.14814\/phy2.70262","archive":["Portico"],"relation":{},"ISSN":["2051-817X","2051-817X"],"issn-type":[{"value":"2051-817X","type":"print"},{"value":"2051-817X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"2024-11-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70262"}}