{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T04:04:39Z","timestamp":1749182679033,"version":"3.41.0"},"reference-count":71,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":46,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec><jats:label\/><jats:p>Functionally characterized lncRNAs play critical roles in cancer progression but the potential relationship between lncRNAs and herbal medicine is yet to be known. To identify this association by RNA\u2010seq data for breast and prostate cancer, a co\u2010expression network in response to herbal medicines was performed. GO terms and pathway analyses on differential co\u2010expressed mRNAs revealed that lncRNAs were widely co\u2010expressed with metabolic process genes. On the other hand, various machine learning\u2010based prediction systems on the differential co\u2010expressed lncRNAs were implemented. Results show that the Deep Learning model could accurately forecast cancer\u2010related lncRNAs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Background<\/jats:title><jats:p>Accumulating evidence shows that long non\u2010coding RNAs (lncRNAs) play critical roles in cancer progression. The possible association between lncRNAs and herbal medicine is yet to be known. This study aims to identify medicinal herbs associated with lncRNAs by RNA\u2010seq data for breast and prostate cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To develop the optimal approach for identifying cancer\u2010related lncRNAs, we implemented two steps: (1) applying protein\u2013protein interaction (PPI), Gene Ontology (GO), and pathway analyses, and (2) applying attribute weighting and finding the efficient classification model of the machine learning approach.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the first step, GO terms and pathway analyses on differential co\u2010expressed mRNAs revealed that lncRNAs were widely co\u2010expressed with metabolic process genes. We identified two hub lncRNA\u2010mRNA networks that implicate lncRNAs associated with breast and prostate cancer. In the second step, we implemented various machine learning\u2010based prediction systems (Decision Tree, Random Forest, Deep Learning, and Gradient\u2010Boosted Tree) on the non\u2010transformed and Z\u2010standardized differential co\u2010expressed lncRNAs. Based on five\u2010fold cross\u2010validation, we obtained high accuracy (91.11%), high sensitivity (88.33%), and high specificity (93.33%) in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study. As data originally came from different cell lines at different durations of herbal treatment intervention, we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs. Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list. Besides, we identified one known lncRNAs, downregulated RNA in cancer (DRAIC), as an essential feature.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer (PC) and breast cancer (BC) in common.<\/jats:p><\/jats:sec>","DOI":"10.15302\/j-qb-023-0333","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T02:45:22Z","timestamp":1689907522000},"page":"343-358","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pattern discovery of long non\u2010coding RNAs associated with the herbal treatments in breast and prostate cancers"],"prefix":"10.1002","volume":"11","author":[{"given":"Elham Dalalbashi","family":"Esfahani","sequence":"first","affiliation":[{"name":"Institute of Biotechnology Shiraz University  Shiraz 7196484334 Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esmaeil","family":"Ebrahimie","sequence":"additional","affiliation":[{"name":"Genomics Research Platform School of Agriculture Biomedicine and Environment La Trobe University  Melbourne Victoria 3086 Australia"},{"name":"School of Animal and Veterinary Sciences The University of Adelaide  South Australia 5005 Australia"},{"name":"School of BioSciences The University of Melbourne  Victoria 3052 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Niazi","sequence":"additional","affiliation":[{"name":"Institute of Biotechnology Shiraz University  Shiraz 7196484334 Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manijeh Mohammadi","family":"Dehcheshmeh","sequence":"additional","affiliation":[{"name":"Genomics Research Platform School of Agriculture Biomedicine and Environment La Trobe University  Melbourne Victoria 3086 Australia"},{"name":"School of Animal and Veterinary Sciences The University of Adelaide  South Australia 5005 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2013.00177"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsps.2019.02.004"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopha.2022.113383"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/2156587217696927"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apjtb.2017.10.016"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1210\/er.19.4.365"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep32731"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-019-6055-9"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-18-2169"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ncrna.2022.02.004"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1042\/BSR20181634"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41413-019-0048-9"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1042\/BSR20180772"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2017.00305"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.15721"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mad.2019.111177"},{"key":"e_1_2_10_18_1","first-page":"49","article-title":"Co\u2010expression network analysis of human lncRNAs and cancer genes","volume":"13","author":"Cogill S. 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