{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T23:23:46Z","timestamp":1779319426737,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819619061","type":"print"},{"value":"9789819619078","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-1907-8_23","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:55:24Z","timestamp":1740426924000},"page":"233-242","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Nuclear Norm Regularization Model for Prediction of Potential Small Molecule\u2013miRNA Associations"],"prefix":"10.1007","author":[{"given":"Ran","family":"Tao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HongJie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cen","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","volume":"116","author":"DP Bartel","year":"2004","unstructured":"Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281\u2013297 (2004)","journal-title":"Cell"},{"key":"23_CR2","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1126\/science.1066315","volume":"294","author":"G Ruvkun","year":"2001","unstructured":"Ruvkun, G.: Glimpses of a tiny RNA world. Science 294, 797\u2013799 (2001)","journal-title":"Science"},{"key":"23_CR3","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1038\/s41580-018-0045-7","volume":"20","author":"LF Gebert","year":"2019","unstructured":"Gebert, L.F., MacRae, I.J.: Regulation of MicroRNA function in animals. Nat. Rev. Mol. Cell Biol. 20, 21\u201337 (2019)","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"23_CR4","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1016\/j.neunet.2023.11.018","volume":"169","author":"H Wu","year":"2023","unstructured":"Wu, H., Liu, J., Jiang, T., et al.: Attention MGT-DTA: a multi-modal drug-target affinity prediction using graph transformer and attention mechanism. Neural Netw. 169, 623\u2013636 (2023)","journal-title":"Neural Netw."},{"key":"23_CR5","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.cell.2009.01.002","volume":"136","author":"DP Bartel","year":"2009","unstructured":"Bartel, D.P.: MicroRNAs: target recognition and regulatory functions. Cell 136, 215\u2013233 (2009)","journal-title":"Cell"},{"key":"23_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107094","volume":"164","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Guan, S., Jiang, T., et al.: DNA protein binding recognition based on lifelong learning. Comput. Biol. Med. 164, 107094 (2023)","journal-title":"Comput. Biol. Med."},{"key":"23_CR7","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1126\/science.1065329","volume":"294","author":"RC Lee","year":"2001","unstructured":"Lee, R.C., Ambros, V.: An extensive class of small RNAs in Caenorhabditis elegans. Science 294, 862\u2013864 (2001)","journal-title":"Science"},{"key":"23_CR8","doi-asserted-by":"publisher","first-page":"5900","DOI":"10.1021\/acs.jmedchem.7b01891","volume":"61","author":"Y Naro","year":"2018","unstructured":"Naro, Y., et al.: Small molecule inhibition of MicroRNA miR-21 rescues chemosensitivity of renal-cell carcinoma to topotecan. J. Med. Chem. 61, 5900\u20135909 (2018)","journal-title":"J. Med. Chem."},{"key":"23_CR9","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1038\/srep00282","volume":"2","author":"W Jiang","year":"2012","unstructured":"Jiang, W., Chen, X., Liao, M., et al.: Identification of links between small molecules and MiRNAs in human cancers based on transcriptional responses. Sci. Rep. 2, 282 (2012)","journal-title":"Sci. Rep."},{"key":"23_CR10","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1038\/clpt.2010.46","volume":"87","author":"S Zhang","year":"2010","unstructured":"Zhang, S., Chen, L., Jung, E., et al.: Targeting MicroRNAs with small molecules: from dream to reality. Clin. Pharmacol. Ther. 87, 754\u2013758 (2010)","journal-title":"Clin. Pharmacol. Ther."},{"key":"23_CR11","doi-asserted-by":"publisher","first-page":"7482","DOI":"10.1002\/anie.200801555","volume":"47","author":"K Gumireddy","year":"2008","unstructured":"Gumireddy, K., Young, D., Xiong, X., Hogenesch, J.B., Huang, Q., Deiters, A.: Small-molecule inhibitors of MicroRNA miR-21 function. Angew. Chem. Int. Ed. 47, 7482\u20137484 (2008)","journal-title":"Angew. Chem. Int. Ed."},{"key":"23_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41419-018-0677-8","volume":"9","author":"X Liu","year":"2018","unstructured":"Liu, X., Zhang, F., Shang, J., et al.: Renal inhibition of MiR-181a ameliorates 5-f Luorouracil-induced mesangial cell apoptosis and nephrotoxicity. Cell Death Dis. 9, 1\u201314 (2018)","journal-title":"Cell Death Dis."},{"key":"23_CR13","unstructured":"Chen, X., Guan, N., Sun, Y., et al.: MicroRNA-small molecule association identification: from experimental results to computational models. Brief. Bioinform. 21, 47\u201361 (2020)"},{"key":"23_CR14","doi-asserted-by":"publisher","first-page":"3638","DOI":"10.1093\/bioinformatics\/btv417","volume":"31","author":"Y Lv","year":"2015","unstructured":"Lv, Y., Wang, S., Meng, F., et al.: Identifying novel associations between small molecules and MiRNAs based on integrated molecular networks. Bioinformatics 31, 3638\u20133644 (2015)","journal-title":"Bioinformatics"},{"key":"23_CR15","doi-asserted-by":"publisher","first-page":"45584","DOI":"10.18632\/oncotarget.10052","volume":"7","author":"J Li","year":"2016","unstructured":"Li, J., Lei, K., Wu, Z., et al.: Network-based identification of MicroRNAs as potential pharmacogenomic biomarkers for anticancer drugs. Oncotarget 7, 45584\u201345596 (2016)","journal-title":"Oncotarget"},{"key":"23_CR16","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.3389\/fphar.2018.01152","volume":"9","author":"N Guan","year":"2018","unstructured":"Guan, N., Sun, Y., Ming, Z., et al.: Prediction of potential small molecule-associated MicroRNAs using Graphlet interaction. Front. Pharmacol. 9, 1152 (2018)","journal-title":"Front. Pharmacol."},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhou, C., Wang, C., et al.: Predicting potential small molecule\u2013MiRNA associations based on bounded nuclear norm regularization. Brief. Bioinform. 22(6), bbab328 (2021)","DOI":"10.1093\/bib\/bbab328"},{"key":"23_CR18","doi-asserted-by":"publisher","first-page":"3157","DOI":"10.1021\/acs.molpharmaceut.9b00384","volume":"16","author":"J Yin","year":"2019","unstructured":"Yin, J., Chen, X., Wang, C., et al.: Prediction of small molecule\u2013MicroRNA associations by sparse learning and heterogeneous graph inference. Mol. Pharm. 16, 3157\u20133166 (2019)","journal-title":"Mol. Pharm."},{"key":"23_CR19","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1098\/rsif.2011.0551","volume":"9","author":"S Lv","year":"2012","unstructured":"Lv, S., Li, Y., Wang, Q., et al.: A novel method to quantify gene set functional association based on gene ontology. J. R. Soc. Interface. 9, 1063\u20131072 (2012)","journal-title":"J. R. Soc. Interface."},{"key":"23_CR20","doi-asserted-by":"publisher","first-page":"11853","DOI":"10.1021\/ja036030u","volume":"125","author":"M Hattori","year":"2003","unstructured":"Hattori, M., Okuno, Y., Goto, S., et al.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125, 11853\u201311865 (2003)","journal-title":"J. Am. Chem. Soc."},{"key":"23_CR21","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1038\/msb.2011.26","volume":"7","author":"A Gottlieb","year":"2011","unstructured":"Gottlieb, A., Stein, G.Y., Ruppin, E., et al.: PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 7, 496 (2011)","journal-title":"Mol. Syst. Biol."},{"key":"23_CR22","doi-asserted-by":"publisher","first-page":"308","DOI":"10.26599\/BDMA.2018.9020008","volume":"1","author":"A Ramlatchan","year":"2018","unstructured":"Ramlatchan, A., Yang, M., Liu, Q., et al.: A survey of matrix completion methods for recommendation systems. Big Data Min. Anal. 1, 308\u2013323 (2018)","journal-title":"Big Data Min. Anal."},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Sun, C., Dai, R.: An iterative approach to rank minimization problems. IEEE Conf. Decis. Control, 3317\u20133323 (2015)","DOI":"10.1109\/CDC.2015.7402718"},{"key":"23_CR24","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s10107-012-0540-0","volume":"141","author":"E Candes","year":"2013","unstructured":"Candes, E., Recht, B.: Simple bounds for recovering low complexity models. Math. Program. 141, 577\u2013589 (2013)","journal-title":"Math. Program."},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Candes, E.J., Plan, Y.: Matrix completion with noise. Proc. IEEE 98, 925\u201336 (2010)","DOI":"10.1109\/JPROC.2009.2035722"},{"key":"23_CR26","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1090\/S0025-5718-2012-02598-1","volume":"82","author":"J Yang","year":"2011","unstructured":"Yang, J., Yuan, X.: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization. Math. Comput. 82, 301\u2013329 (2011)","journal-title":"Math. Comput."},{"key":"23_CR27","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1093\/imanum\/drq039","volume":"32","author":"C Chen","year":"2011","unstructured":"Chen, C., He, B., Yuan, X.: Matrix completion via an alternating direction method. IMA J. Numer. Anal. 32, 227\u2013245 (2011)","journal-title":"IMA J. Numer. Anal."},{"key":"23_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2010","unstructured":"Boyd, S.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1\u2013122 (2010)","journal-title":"Found. Trends Mach. Learn."},{"key":"23_CR29","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1109\/TPAMI.2012.271","volume":"35","author":"Y Hu","year":"2013","unstructured":"Hu, Y., Zhang, D., Ye, J., et al.: Fast and accurate matrix completion via truncated nuclear norm regularization. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2117\u20132130 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR30","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s10107-009-0306-5","volume":"128","author":"S Ma","year":"2011","unstructured":"Ma, S., Goldfarb, D., Chen, L.: Fixed point and Bregman iterative methods for matrix rank minimization. Math. Program. 128, 321\u2013353 (2011)","journal-title":"Math. Program."},{"key":"23_CR31","first-page":"615","volume":"6","author":"KC Toh","year":"2010","unstructured":"Toh, K.C., Yun, S.: An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems. Pacific J. Optim. 6, 615\u2013640 (2010)","journal-title":"Pacific J. Optim."},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Guan, S., Zou, Q., Wu, H., et al.: Protein-DNA binding residues prediction using a deep learning model with hierarchical feature extraction. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2022)","DOI":"10.1109\/TCBB.2022.3190933"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Cai, J.F., Cand\u00e8s, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. Siam J. Optim. 20,1956\u20131982 (2010)","DOI":"10.1137\/080738970"},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Lu, Y., Zhang, R., Jiang, T., et al.: TrGPCR: GPCR-ligand binding affinity predicting based on dynamic deep transfer learning. IEEE J. Biomed. Health Inform. (2023). https:\/\/doi.org\/10.1109\/JBHI.2023.330792","DOI":"10.1109\/JBHI.2023.3307928"},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Liu, J., Guan, S., Zou, Q., Wu, H., Prayag Tiwari, Ding, Y.: AMDGT: Attention aware multi-modal fusion using a dual graph transformer for drug\u2013disease associations prediction. Knowl.-Based Syst. 284 (2024)","DOI":"10.1016\/j.knosys.2023.111329"},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Liu, J., Hu, F., Zou, Q., Tiwari, P., Wu, H., Ding, Y.: Drug repositioning by multi-aspect heterogeneous graph contrastive learning and positive-fusion negative sampling strategy. Inf. Fus. 112 (2024)","DOI":"10.1016\/j.inffus.2024.102563"}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1907-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:55:29Z","timestamp":1740426929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1907-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819619061","9789819619078"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1907-8_23","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhenzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.org.cn\/2024\/Organization.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}