{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:55:26Z","timestamp":1757620526760,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819506941"},{"type":"electronic","value":"9789819506958"}],"license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-0695-8_2","type":"book-chapter","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:54:13Z","timestamp":1753966453000},"page":"14-25","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LGFMDA: miRNA-Disease Association Prediction with\u00a0Local and\u00a0Global Feature Representation Learning"],"prefix":"10.1007","author":[{"given":"Chunyang","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Yuanbo","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xuehua","family":"Bi","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Quah, S., Subramanian, G., Tan, J.S., Utami, K.H., Sampath, P.: Micrornas: a symphony orchestrating evolution and disease dynamics. Trends Mol. Med. (2024)","DOI":"10.1016\/j.molmed.2024.07.004"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Huang, Z., et al.: Hmdd v3. 0: a database for experimentally supported human microrna\u2013disease associations. Nucleic Acids Res. 47(D1), D1013\u2013D1017 (2019)","DOI":"10.1093\/nar\/gky1010"},{"issue":"D1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"D155","DOI":"10.1093\/nar\/gky1141","volume":"47","author":"A Kozomara","year":"2019","unstructured":"Kozomara, A., Birgaoanu, M., Griffiths-Jones, S.: mirbase: from microrna sequences to function. Nucleic Acids Res. 47(D1), D155\u2013D162 (2019)","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"2_CR4","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1093\/bib\/bbx130","volume":"20","author":"X Chen","year":"2019","unstructured":"Chen, X., Xie, D., Zhao, Q., You, Z.-H.: Micrornas and complex diseases: from experimental results to computational models. Brief. Bioinform. 20(2), 515\u2013539 (2019)","journal-title":"Brief. Bioinform."},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1752-0509-4-S1-S2","volume":"4","author":"Q Jiang","year":"2010","unstructured":"Jiang, Q., et al.: Prioritization of disease micrornas through a human phenome-micrornaome network. BMC Syst. Biol. 4, 1\u20139 (2010)","journal-title":"BMC Syst. Biol."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Wbsmda: within and between score for mirna-disease association prediction. Sci. Rep. 6(1), 21106 (2016)","DOI":"10.1038\/srep21106"},{"issue":"7","key":"2_CR7","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1080\/15476286.2017.1312226","volume":"14","author":"X Chen","year":"2017","unstructured":"Chen, X., Qiao-Feng, W., Yan, G.-Y.: Rknnmda: ranking-based knn for mirna-disease association prediction. RNA Biol. 14(7), 952\u2013962 (2017)","journal-title":"RNA Biol."},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"104706","DOI":"10.1016\/j.compbiomed.2021.104706","volume":"136","author":"Q Dai","year":"2021","unstructured":"Dai, Q., et al.: Mda-cf: predicting mirna-disease associations based on a cascade forest model by fusing multi-source information. Comput. Biol. Med. 136, 104706 (2021)","journal-title":"Comput. Biol. Med."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Ding, Y., Lei, X., Liao, B., Wu, F.X.: Mlrdfm: a multi-view laplacian regularized deepfm model for predicting mirna-disease associations. Briefings Bioinf. 23(3), bbac079 (2022)","DOI":"10.1093\/bib\/bbac079"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Zhong, T., Li, Z., You, Z.H., Nie, R., Zhao, H.: Predicting mirna\u2013disease associations based on graph random propagation network and attention network. Briefings Bioinf. 23(2), bbab589 (2022)","DOI":"10.1093\/bib\/bbab589"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"107585","DOI":"10.1016\/j.compbiomed.2023.107585","volume":"167","author":"B Dong","year":"2023","unstructured":"Dong, B., Sun, W., Dali, X., Wang, G., Zhang, T.: Mdformer: a transformer-based method for predicting mirna-disease associations using multi-source feature fusion and maximal meta-path instances encoding. Comput. Biol. Med. 167, 107585 (2023)","journal-title":"Comput. Biol. Med."},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Sheng, N., Xie, X., Wang, Y., Huang, L., Zhang, S., Gao, L., Wang, H.: A survey of deep learning for detecting mirna-disease associations: databases, computational methods, challenges, and future directions. IEEE\/ACM Trans. Comput. Biol. Bioinf. (2024)","DOI":"10.1109\/TCBB.2024.3351752"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Li, Z.W., Wang, Q.K., Yuan, C.A., Han, P.Y., You, Z.H., Wang, L.: Predicting mirna-disease associations by graph representation learning based on jumping knowledge networks. IEEE\/ACM Trans. Comput. Biol. Bioinf. (2022)","DOI":"10.1109\/TCBB.2022.3196394"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Bi, X., Jiang, C., Yan, C., Zhao, K., Zhang, L., Wang, J.: Esgc-mda: Identifying mirna-disease associations using enhanced simple graph convolutional networks. IEEE\/ACM Trans. Comput. Biol. Bioinf. (2024)","DOI":"10.1109\/TCBB.2024.3486911"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, H., Li, Z., You, Z.H., Nie, R., Zhong, T.: Predicting mirna-disease associations based on neighbor selection graph attention networks. IEEE\/ACM Trans. Comput. Biol. Bioinf. 20(2), 1298\u20131307 (2022)","DOI":"10.1109\/TCBB.2022.3204726"},{"key":"2_CR16","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861\u20136871. PMLR (2019)"},{"key":"2_CR17","first-page":"20321","volume":"34","author":"W Zhang","year":"2021","unstructured":"Zhang, W., et al.: Node dependent local smoothing for scalable graph learning. Adv. Neural. Inf. Process. Syst. 34, 20321\u201320332 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Zhao, B.W., Su, X.R., Hu, P.W., Huang, Y.A., You, Z.H., Hu, L.: igrldti: an improved graph representation learning method for predicting drug\u2013target interactions over heterogeneous biological information network. Bioinformatics 39(8), btad451 (2023)","DOI":"10.1093\/bioinformatics\/btad451"},{"key":"2_CR19","unstructured":"Ma, L., et al.: Graph inductive biases in transformers without message passing. In: International Conference on Machine Learning, pp. 23321\u201323337. PMLR (2023)"},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1186\/s12859-021-04135-2","volume":"22","author":"D Liu","year":"2021","unstructured":"Liu, D., Huang, Y., Nie, W., Zhang, J., Deng, L.: Smalf: mirna-disease associations prediction based on stacked autoencoder and xgboost. BMC Bioinf. 22(1), 219 (2021)","journal-title":"BMC Bioinf."},{"issue":"5","key":"2_CR21","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1093\/bfgp\/elad010","volume":"22","author":"H Hua","year":"2023","unstructured":"Hua, H., et al.: Adaptive deep propagation graph neural network for predicting mirna-disease associations. Brief. Funct. Genomics 22(5), 453\u2013462 (2023)","journal-title":"Brief. Funct. Genomics"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0695-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T09:38:55Z","timestamp":1757324335000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0695-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"ISBN":["9789819506941","9789819506958"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0695-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"assertion":[{"value":"1 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Helsinki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.helsinki.fi\/en\/conferences\/isbra2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}