{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:06:24Z","timestamp":1776488784620,"version":"3.51.2"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003968","name":"Iran National Science Foundation","doi-asserted-by":"publisher","award":["4025738"],"award-info":[{"award-number":["4025738"]}],"id":[{"id":"10.13039\/501100003968","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Pharmaceutical and Biomedical Analysis"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.jpba.2024.116377","type":"journal-article","created":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T14:29:00Z","timestamp":1721572140000},"page":"116377","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer"],"prefix":"10.1016","volume":"249","author":[{"given":"Maryam","family":"Kashi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadi","family":"Parastar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.jpba.2024.116377_bib1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1177\/0969141321993866","article-title":"Benefits and harms of breast cancer mammography screening for women at average risk of breast cancer: a systematic review for the European Commission Initiative on Breast Cancer","volume":"28","author":"Canelo-Aybar","year":"2021","journal-title":"J. Med. Screen."},{"key":"10.1016\/j.jpba.2024.116377_bib2","doi-asserted-by":"crossref","first-page":"975","DOI":"10.3390\/cancers12040976","article-title":"Worldwide review and meta-analysis of cohort studies measuring the effect of mammography screening programmes on incidence-based breast cancer mortality","volume":"12","author":"Dibden","year":"2020","journal-title":"Cancers"},{"key":"10.1016\/j.jpba.2024.116377_bib3","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1007\/s11306-015-0793-8","article-title":"Forecasting individual breast cancer risk using plasma metabolomics and biocontours","volume":"11","author":"Bro","year":"2015","journal-title":"Metabolomics"},{"key":"10.1016\/j.jpba.2024.116377_bib4","doi-asserted-by":"crossref","first-page":"643","DOI":"10.3390\/metabo12070643","article-title":"Metabolomics of breast cancer: a review","volume":"12","author":"Subramani","year":"2022","journal-title":"Metabolites"},{"key":"10.1016\/j.jpba.2024.116377_bib5","doi-asserted-by":"crossref","first-page":"989","DOI":"10.3390\/metabo13090989","article-title":"Peripheral blood serum NMR metabolomics is a powerful tool to discriminate benign and malignant ovarian tumors","volume":"13","author":"Nunes","year":"2023","journal-title":"Metabolites"},{"key":"10.1016\/j.jpba.2024.116377_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2023.104826","article-title":"Discrimination of molecular subtypes of breast cancer with ATR-FTIR spectroscopy in blood plasma coupled with partial least square-artificial neural network discriminant analysis (PLS-ANNDA)","volume":"237","author":"de Souza","year":"2023","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.jpba.2024.116377_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijleo.2020.164225","article-title":"Use of FT-IR spectroscopy combined with SVM as a screening tool to identify invasive ductal carcinoma in breast cancer","volume":"204","author":"Liu","year":"2020","journal-title":"Optik"},{"key":"10.1016\/j.jpba.2024.116377_bib8","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.procs.2023.01.025","article-title":"Diagnosis of breast cancer using random forests","volume":"218","author":"Minnoor","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.jpba.2024.116377_bib9","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.jpba.2018.07.024","article-title":"Metabolic fingerprinting in breast cancer stages through 1H NMR spectroscopy-based metabolomic analysis of plasma","volume":"160","author":"Suman","year":"2018","journal-title":"J. Pharm. Biomed. Anal."},{"key":"10.1016\/j.jpba.2024.116377_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.microc.2020.105559","article-title":"Optimization and validation of a HILIC-LC-ESI-MS\/MS method for the simultaneous analysis of targeted metabolites: cross validation of untargeted metabolomic studies for early diagnosis of breast cancer","volume":"159","author":"Re\u00e7ber","year":"2020","journal-title":"Microchem. J."},{"key":"10.1016\/j.jpba.2024.116377_bib11","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-39215-1","article-title":"Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline","volume":"13","author":"Santaliz-Casiano","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jpba.2024.116377_bib12","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1038\/s41598-023-50866-y","article-title":"1H NMR metabolomics analysis identifies hypoxanthine as a novel metastasis-associated metabolite in breast cancer","volume":"14","author":"Shakartalla","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jpba.2024.116377_bib13","first-page":"45","article-title":"Kohonen and counterpropagation neural networks applied for mapping and interpretation of ir spectra","volume":"458","author":"Zupan","year":"2008","journal-title":"Methods Mol. Biol."},{"key":"10.1016\/j.jpba.2024.116377_bib14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.chemolab.2012.07.005","article-title":"A MATLAB toolbox for self organizing maps and supervised neural network learning strategies","volume":"118","author":"Ballabio","year":"2012","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.jpba.2024.116377_bib15","first-page":"1","article-title":"Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization","volume":"13","author":"Wang","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jpba.2024.116377_bib16","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.measurement.2019.01.013","article-title":"Application of improved SOM network in gene data cluster analysis","volume":"145","author":"Nan","year":"2019","journal-title":"Measurement"},{"key":"10.1016\/j.jpba.2024.116377_bib17","first-page":"62","article-title":"Projection of high-dimensional genome-wide expression on SOM transcriptome landscapes","volume":"2","author":"Nikoghosyan","year":"2021","journal-title":"Bio. Med. Inform."},{"key":"10.1016\/j.jpba.2024.116377_bib18","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.chemolab.2009.05.007","article-title":"The Kohonen and CP-ANN toolbox: a collection of MATLAB modules for self organizing maps and counterpropagation artificial neural networks","volume":"98","author":"Ballabio","year":"2009","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.jpba.2024.116377_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2022.104519","article-title":"Untargeted metabolomics based on nuclear magnetic resonance spectroscopy and multivariate classification techniques for identifying metabolites associated with breast cancer patients","volume":"223","author":"Esmaeili","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.jpba.2024.116377_bib20","doi-asserted-by":"crossref","first-page":"7832","DOI":"10.1016\/j.chroma.2011.08.086","article-title":"icoshift: an effective tool for the alignment of chromatographic data","volume":"1218","author":"Tomasi","year":"2011","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.jpba.2024.116377_bib21","doi-asserted-by":"crossref","DOI":"10.1074\/mcp.M110.002717","article-title":"High-mannose glycans are elevated during breast cancer progression","volume":"10","author":"de Leoz","year":"2011","journal-title":"Mol. Cell. Proteom."},{"key":"10.1016\/j.jpba.2024.116377_bib22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/00365513.2018.1542541","article-title":"The decrease of some serum free amino acids can predict breast cancer diagnosis and progression","volume":"79","author":"Eniu","year":"2019","journal-title":"Scand. J. Clin. Lab. Invest."},{"key":"10.1016\/j.jpba.2024.116377_bib23","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3390\/ijms242015369","article-title":"The significance of microenvironmental and circulating lactate in breast cancer","volume":"24","author":"Frisardi","year":"2023","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.jpba.2024.116377_bib24","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12859-019-3310-7","article-title":"So you think you can PLS-DA?","volume":"21","author":"Ruiz-Perez","year":"2020","journal-title":"BMC Bioinforma."},{"key":"10.1016\/j.jpba.2024.116377_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2021.104307","article-title":"PLS regression algorithms in the presence of nonlinearity","volume":"213","author":"Cook","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.jpba.2024.116377_bib26","series-title":"International Conference on Intelligent Sustainable Systems (ICISS)","first-page":"24","article-title":"A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification","author":"Ghosh","year":"2019"},{"key":"10.1016\/j.jpba.2024.116377_bib27","doi-asserted-by":"crossref","first-page":"S13875","DOI":"10.4137\/CIN.S13875","article-title":"Overcome support vector machine diagnosis overfitting","volume":"13s1","author":"Han","year":"2014","journal-title":"Cancer Inf."}],"container-title":["Journal of Pharmaceutical and Biomedical Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0731708524004175?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0731708524004175?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T15:54:07Z","timestamp":1731426847000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0731708524004175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":27,"alternative-id":["S0731708524004175"],"URL":"https:\/\/doi.org\/10.1016\/j.jpba.2024.116377","relation":{},"ISSN":["0731-7085"],"issn-type":[{"value":"0731-7085","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer","name":"articletitle","label":"Article Title"},{"value":"Journal of Pharmaceutical and Biomedical Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jpba.2024.116377","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116377"}}