{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:18:51Z","timestamp":1783509531225,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819224937","type":"print"},{"value":"9789819224944","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-2494-4_6","type":"book-chapter","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T10:53:55Z","timestamp":1783508035000},"page":"88-104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Non-negative Matrix Factorization Algorithm for\u00a0Acupuncture Omics Data Clustering"],"prefix":"10.1007","author":[{"given":"Zonglin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianbo","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaofeng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dexian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuguang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Rashid, A.B., Kausik, A.K.: AI revolutionizing industries worldwide: a comprehensive overview of its diverse applications. Hybrid Adv. 100277 (2024)","DOI":"10.1016\/j.hybadv.2024.100277"},{"issue":"7972","key":"6_CR2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41586-023-06221-2","volume":"620","author":"H Wang","year":"2023","unstructured":"Wang, H., et al.: Scientific discovery in the age of artificial intelligence. Nature 620(7972), 47\u201360 (2023)","journal-title":"Nature"},{"issue":"41","key":"6_CR3","doi-asserted-by":"publisher","first-page":"16844","DOI":"10.1039\/D4SC04107K","volume":"15","author":"Z Song","year":"2024","unstructured":"Song, Z., Chen, G., Chen, C.Y.C.: AI empowering traditional Chinese medicine? Chem. Sci. 15(41), 16844\u201316886 (2024)","journal-title":"Chem. Sci."},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Liang, Z., Zhang, G., Li, Z., Yin, J., Fu, W.: Deep learning for acupuncture point selection patterns based on veteran doctor experience of Chinese medicine. In: 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 396\u2013401 (2012)","DOI":"10.1109\/BIBMW.2012.6470346"},{"issue":"9","key":"6_CR5","doi-asserted-by":"publisher","first-page":"1098766","DOI":"10.3389\/fnmol.2022.1098766","volume":"15","author":"L Liu","year":"2023","unstructured":"Liu, L., et al.: Circulating exosomal microrna profiles in migraine patients receiving acupuncture treatment: a placebo-controlled clinical trial. Front. Mol. Neurosci. 15(9), 1098766 (2023)","journal-title":"Front. Mol. Neurosci."},{"issue":"4","key":"6_CR6","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1097\/HM9.0000000000000077","volume":"3","author":"Y Bao","year":"2023","unstructured":"Bao, Y., et al.: Intelligent acupuncture: data-driven revolution of traditional Chinese medicine. Acupuncture Herbal Med. 3(4), 271\u2013284 (2023)","journal-title":"Acupuncture Herbal Med."},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Ran, R., Brubaker, D.K.: Enhanced annotation of CD45RA to distinguish T cell subsets in single-cell RNA-seq via machine learning. Bioinform. Adv. 3(1), vbad159 (2023)","DOI":"10.1093\/bioadv\/vbad159"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Yang, H., Wu, R., Nakata, M., An, Z., Ge, Q.-W.: Ensemble learning-based approach for deciding acupoints in acupuncture and moxibustion treatment. In: 2023 International Technical Conference on Circuits\/Systems, Computers, and Communications, pp. 1\u20136 (2023)","DOI":"10.1109\/ITC-CSCC58803.2023.10212789"},{"key":"6_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctim.2024.103110","volume":"88","author":"C Liu","year":"2025","unstructured":"Liu, C., et al.: A comprehensive overview of acupuncture therapy over the past 20 years: machine learning-based bibliometric analysis. Complement. Ther. Med. 88, 103110 (2025)","journal-title":"Complement. Ther. Med."},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Zhao, P., Pang, L., Cao, S., Cao, Z.: Deep learning-based patient in-position detection for acupuncture treatment. In: Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, vol. 12857, pp. 157\u2013165 (2024)","DOI":"10.1117\/12.3023364"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"1448119","DOI":"10.3389\/fendo.2024.1448119","volume":"15","author":"KSJ Wu","year":"2024","unstructured":"Wu, K.S.J.: Deciphering the role of lipid metabolism-related genes in Alzheimer\u2019s disease: a machine learning approach integrating traditional Chinese medicine. Front. Endocrinol. 15, 1448119 (2024)","journal-title":"Front. Endocrinol."},{"issue":"1","key":"6_CR12","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s40122-024-00700-8","volume":"14","author":"Y Lian","year":"2025","unstructured":"Lian, Y., Shi, Y., Shang, H., Zhan, H.: Predicting treatment outcomes in patients with low back pain using gene signature-based machine learning models. Pain Ther. 14(1), 359\u2013373 (2025)","journal-title":"Pain Ther."},{"issue":"1","key":"6_CR13","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/s42003-023-05669-2","volume":"7","author":"W Zhou","year":"2024","unstructured":"Zhou, W., et al.: Characterizing immune variation and diagnostic indicators of preeclampsia by single-cell RNA sequencing and machine learning. Commun. Biol. 7(1), 32 (2024)","journal-title":"Commun. Biol."},{"issue":"2","key":"6_CR14","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s12920-016-0207-4","volume":"9","author":"Z Liang","year":"2016","unstructured":"Liang, Z., Huang, J.X., Zeng, X., Zhang, G.: DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions. BMC Med. Genomics 9(2), 48 (2016)","journal-title":"BMC Med. Genomics"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, C., Tam, C.W., Tang, G., Chen, Y., Wang, N., Feng, Y.: Spatial transcriptomic analysis using R-based computational machine learning reveals the genetic profile of Yang or Yin deficiency syndrome in Chinese medicine theory. Evidence-Based Compl. Alternative Med. 5503181 (2022)","DOI":"10.1155\/2022\/5503181"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Fujii, Y.R.: Deep learning of miRNAs for therapeutic applications. In: The MicroRNA 2000 Transformer: Quantum Computing and Artificial Intelligence for Health, pp. 207\u2013222 (2023)","DOI":"10.1007\/978-981-99-3165-1_11"},{"issue":"12","key":"6_CR17","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1038\/s41592-024-02487-0","volume":"21","author":"T Shen","year":"2024","unstructured":"Shen, T., et al.: Accurate RNA 3D structure prediction using a language model-based deep learning approach. Nat. Methods 21(12), 2287\u20132298 (2024)","journal-title":"Nat. Methods"},{"issue":"1","key":"6_CR18","doi-asserted-by":"publisher","first-page":"26503","DOI":"10.1038\/s41598-024-78553-6","volume":"14","author":"W DeGroat","year":"2024","unstructured":"DeGroat, W., et al.: Multimodal AI\/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases. Sci. Rep. 14(1), 26503 (2024)","journal-title":"Sci. Rep."},{"issue":"6","key":"6_CR19","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1038\/s12276-024-01243-w","volume":"56","author":"H Hwang","year":"2024","unstructured":"Hwang, H., Jeon, H., Yeo, N., Baek, D.: Big data and deep learning for RNA biology. Experimental Mol. Med. 56(6), 1293\u20131321 (2024)","journal-title":"Experimental Mol. Med."},{"issue":"1","key":"6_CR20","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1038\/s41598-025-85618-7","volume":"15","author":"Y Liu","year":"2025","unstructured":"Liu, Y., et al.: Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma. Sci. Rep. 15(1), 1691 (2025)","journal-title":"Sci. Rep."},{"issue":"7","key":"6_CR21","first-page":"2110","volume":"67","author":"J Deng","year":"2019","unstructured":"Deng, J., Zeng, W.M., Kong, W., Shi, Y.H., Mou, X.Y., Guo, J.: Multi-constrained joint non-negative matrix factorization with application to imaging genomic study of lung metastasis in soft tissue sarcomas. IEEE Trans. Biomed. Eng. 67(7), 2110\u20132118 (2019)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"6_CR22","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TCBB.2022.3143900","volume":"20","author":"WX Huang","year":"2022","unstructured":"Huang, W.X., Tan, K.W., Zhang, Z.Y., Hu, J.L., Dong, S.B.: A review of fusion methods for omics and imaging data. IEEE\/ACM Trans. Comput. Biol. Bioinf. 20(1), 74\u201393 (2022)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"4","key":"6_CR23","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1109\/TCYB.2025.3534195","volume":"55","author":"ZH Jia","year":"2025","unstructured":"Jia, Z.H., Zhang, Z., Pedrycz, W.: Generation of granular-balls for clustering based on the principle of justifiable granularity. IEEE Trans. Cybern. 55(4), 1687\u20131700 (2025)","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"6_CR24","first-page":"4144","volume":"37","author":"Z Ma","year":"2025","unstructured":"Ma, Z., Wang, J., Nie, F., Li, X.: Large-scale clustering with anchor-based constrained Laplacian rank. IEEE Trans. Knowl. Data Eng. 37(7), 4144\u20134158 (2025)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"6_CR25","doi-asserted-by":"publisher","first-page":"7705","DOI":"10.1038\/s41467-022-35031-9","volume":"13","author":"X Lin","year":"2022","unstructured":"Lin, X., Tian, T., Wei, Z., Hakonarson, H.: Clustering of single-cell multi-omics data with a multimodal deep learning method. Nat. Commun. 13, 7705 (2022)","journal-title":"Nat. Commun."},{"issue":"5","key":"6_CR26","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nrg3433","volume":"14","author":"B Berger","year":"2013","unstructured":"Berger, B., Peng, J., Singh, M.: Computational solutions for omics data. Nat. Rev. Genet. 14(5), 333\u2013346 (2013)","journal-title":"Nat. Rev. Genet."},{"key":"6_CR27","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.neucom.2018.03.072","volume":"324","author":"Y Guo","year":"2019","unstructured":"Guo, Y., Shang, X.Q., Li, Z.H.: Identification of cancer subtypes by integrating multiple types of transcriptomics data with deep learning in breast cancer. Neurocomputing 324, 20\u201330 (2019)","journal-title":"Neurocomputing"},{"issue":"14","key":"6_CR28","doi-asserted-by":"publisher","first-page":"i501","DOI":"10.1093\/bioinformatics\/btz318","volume":"35","author":"H Sharifi-Noghabi","year":"2019","unstructured":"Sharifi-Noghabi, H., Zolotareva, O., Collins, C.C., Ester, M.: MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 35(14), i501\u2013i509 (2019)","journal-title":"Bioinformatics"},{"issue":"7","key":"6_CR29","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.1145\/3584862","volume":"17","author":"D Wang","year":"2023","unstructured":"Wang, D., et al.: A generalized deep learning clustering algorithm based on non-negative matrix factorization. ACM Trans. Knowl. Discov. Data 17(7), 1556\u20134681 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"issue":"1","key":"6_CR30","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TBDATA.2022.3163584","volume":"9","author":"D Wang","year":"2023","unstructured":"Wang, D., Li, T., Deng, P., Liu, J., Huang, W., Zhang, F.: A generalized deep learning algorithm based on NMF for multi-view clustering. IEEE Trans. Big Data 9(1), 328\u2013340 (2023)","journal-title":"IEEE Trans. Big Data"},{"issue":"1","key":"6_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-06921-8","volume":"9","author":"D Ramazzotti","year":"2018","unstructured":"Ramazzotti, D., Lal, A., Wang, B., Batzoglou, S., Sidow, A.: Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat. Commun. 9(1), 1\u201314 (2018)","journal-title":"Nat. Commun."},{"issue":"6","key":"6_CR32","doi-asserted-by":"publisher","first-page":"3065","DOI":"10.1109\/TCBBIO.2025.3612010","volume":"22","author":"H Yang","year":"2025","unstructured":"Yang, H., et al.: Trans-driver: a deep learning approach for cancer driver gene discovery with multi-omics data. IEEE Trans. Comput. Biol. Bioinform. 22(6), 3065\u20133076 (2025)","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Wang, D.X., et al.: Deep multi-view clustering algorithm with integrated auto-encoder and non-negative matrix factorization. Appl. Soft Comput. 114552 (2026)","DOI":"10.1016\/j.asoc.2025.114552"},{"key":"6_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.112738","volume":"162","author":"P Deng","year":"2025","unstructured":"Deng, P., et al.: Symmetric non-negative matrix factorization-based deep representation algorithm for multi-view clustering. Eng. Appl. Artif. Intell. 162, 112738 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"6_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113771","volume":"324","author":"DX Wang","year":"2025","unstructured":"Wang, D.X., et al.: NDRIDC: NMF-based deep representation algorithm for incomplete data clustering. Knowl.-Based Syst. 324, 113771 (2025)","journal-title":"Knowl.-Based Syst."},{"key":"6_CR36","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"D Lee","year":"1999","unstructured":"Lee, D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788\u2013791 (1999)","journal-title":"Nature"},{"issue":"8","key":"6_CR37","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","volume":"33","author":"D Cai","year":"2011","unstructured":"Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1548\u20131560 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Leplat, V., Ang, A.M.S., Gillis, N.: Minimum-volume rank-deficient nonnegative matrix factorizations. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3402\u20133406 (2019)","DOI":"10.1109\/ICASSP.2019.8682280"},{"key":"6_CR39","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neucom.2020.06.049","volume":"412","author":"Q Huang","year":"2020","unstructured":"Huang, Q., Yin, X., Chen, S., Wang, Y., Chen, B.: Robust nonnegative matrix factorization with structure regularization. Neurocomputing 412, 72\u201390 (2020)","journal-title":"Neurocomputing"},{"key":"6_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123645","volume":"249","author":"F Saberi-Movahed","year":"2024","unstructured":"Saberi-Movahed, F., Biswas, B., Tiwari, P., Lehmann, J., Vahdati, S.: Deep nonnegative matrix factorization with joint global and local structure preservation. Expert Syst. Appl. 249, 123645 (2024)","journal-title":"Expert Syst. Appl."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Engineering for Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2494-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T10:53:59Z","timestamp":1783508039000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2494-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"ISBN":["9789819224937","9789819224944"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2494-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"assertion":[{"value":"9 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FLINS-ISKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems and Knowledge Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","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":"iske2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.flins.cc","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}