{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:27:48Z","timestamp":1763058468743,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030608019"},{"type":"electronic","value":"9783030608026"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60802-6_29","type":"book-chapter","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T18:04:35Z","timestamp":1602612275000},"page":"326-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of Autistic Risk Genes Using Developmental Brain Gene Expression Data"],"prefix":"10.1007","author":[{"given":"Zhi-An","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-An","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-Qing","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,5]]},"reference":[{"issue":"6","key":"29_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15585\/mmwr.ss6706a1","volume":"67","author":"J Baio","year":"2018","unstructured":"Baio, J., et al.: Prevalence of autism spectrum disorder among children aged 8 years\u2014autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveill. Summar. 67(6), 1 (2018)","journal-title":"MMWR Surveill. Summar."},{"issue":"11","key":"29_CR2","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1016\/j.jaac.2012.08.018","volume":"51","author":"G Dawson","year":"2012","unstructured":"Dawson, G., et al.: Early behavioral intervention is associated with normalized brain activity in young children with autism. J. Am. Acad. Child Adolescent Psychiatry 51(11), 1150\u20131159 (2012)","journal-title":"J. Am. Acad. Child Adolescent Psychiatry"},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"Huang, Y.-A., Chan, K.C., You, Z.-H., Hu, P., Wang, L., Huang, Z.-A.: Predicting microRNA\u2013disease associations from lncRNA\u2013microRNA interactions via Multiview Multitask Learning. Brief. Bioinf. (2020). https:\/\/doi.org\/10.1093\/bib\/bbaa133","DOI":"10.1093\/bib\/bbaa133"},{"issue":"3","key":"29_CR4","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TCBB.2018.2882423","volume":"16","author":"Z-H You","year":"2018","unstructured":"You, Z.-H., Huang, W.-Z., Zhang, S., Huang, Y.-A., Yu, C.-Q., Li, L.-P.: An efficient ensemble learning approach for predicting protein-protein interactions by integrating protein primary sequence and evolutionary information. IEEE\/ACM Trans. Comput. Biol. Bioinf. 16(3), 809\u2013817 (2018)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"8","key":"29_CR5","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1093\/rheumatology\/keu427","volume":"54","author":"PC Grayson","year":"2015","unstructured":"Grayson, P.C., et al.: Value of commonly measured laboratory tests as biomarkers of disease activity and predictors of relapse in eosinophilic granulomatosis with polyangiitis. Rheumatology 54(8), 1351\u20131359 (2015)","journal-title":"Rheumatology"},{"key":"29_CR6","doi-asserted-by":"publisher","unstructured":"Huang, Z.-A., Zhu, Z., Yau, C.H., Tan, K.C.: Identifying autism spectrum disorder from resting-state fMRI using deep belief network. IEEE Trans. Neural Netw. Learn. Syst. (2020). https:\/\/doi.org\/10.1109\/TNNLS.2020.3007943","DOI":"10.1109\/TNNLS.2020.3007943"},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1146\/annurev.med.60.053107.121225","volume":"60","author":"DH Geschwind","year":"2009","unstructured":"Geschwind, D.H.: Advances in autism. Ann. Rev. Med. 60, 367\u2013380 (2009)","journal-title":"Ann. Rev. Med."},{"issue":"1","key":"29_CR8","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.ajhg.2007.09.005","volume":"82","author":"M Alarc\u00f3n","year":"2008","unstructured":"Alarc\u00f3n, M., et al.: Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene. Am. J. Hum. Genet. 82(1), 150\u2013159 (2008)","journal-title":"Am. J. Hum. Genet."},{"issue":"2","key":"29_CR9","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/nrneurol.2013.278","volume":"10","author":"SS Jeste","year":"2014","unstructured":"Jeste, S.S., Geschwind, D.H.: Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10(2), 74 (2014)","journal-title":"Nat. Rev. Neurol."},{"issue":"1","key":"29_CR10","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1186\/s12859-017-1588-x","volume":"18","author":"Z-A Huang","year":"2017","unstructured":"Huang, Z.-A., Wen, Z., Deng, Q., Chu, Y., Sun, Y., Zhu, Z.: LW-FQZip 2: a parallelized reference-based compression of FASTQ files. BMC Bioinf. 18(1), 179 (2017)","journal-title":"BMC Bioinf."},{"issue":"4","key":"29_CR11","doi-asserted-by":"publisher","first-page":"823","DOI":"10.3390\/molecules23040823","volume":"23","author":"T Wang","year":"2018","unstructured":"Wang, T., Li, L., Huang, Y.-A., Zhang, H., Ma, Y., Zhou, X.: Prediction of protein-protein interactions from amino acid sequences based on continuous and discrete wavelet transform features. Molecules 23(4), 823 (2018)","journal-title":"Molecules"},{"issue":"6","key":"29_CR12","first-page":"17","volume":"11","author":"Z-A Huang","year":"2018","unstructured":"Huang, Z.-A., Huang, Y.-A., You, Z.-H., Zhu, Z., Sun, Y.: Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph. BMC Med. Genom. 11(6), 17\u201327 (2018)","journal-title":"BMC Med. Genom."},{"issue":"5","key":"29_CR13","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1093\/bioinformatics\/btx672","volume":"34","author":"Y-A Huang","year":"2018","unstructured":"Huang, Y.-A., Chan, K.C., You, Z.-H.: Constructing prediction models from expression profiles for large scale lncRNA\u2013miRNA interaction profiling. Bioinformatics 34(5), 812\u2013819 (2018)","journal-title":"Bioinformatics"},{"key":"29_CR14","first-page":"39663","volume":"6","author":"L Shen","year":"2016","unstructured":"Shen, L., Lin, Y., Sun, Z., Yuan, X., Chen, L., Shen, B.: Knowledge-guided bioinformatics model for identifying autism spectrum disorder diagnostic MicroRNA biomarkers. Sci. Reports. 6, 39663 (2016)","journal-title":"Sci. Reports."},{"key":"29_CR15","doi-asserted-by":"publisher","unstructured":"Hu, P., Huang, Y.-A., Chan, K.C., You, Z.-H.: Learning multimodal networks from heterogeneous data for prediction of lncRNA-miRNA interactions. IEEE\/ACM Trans. Comput. Biol. Bioinf. (2019). https:\/\/doi.org\/10.1109\/TCBB.2019.2957094","DOI":"10.1109\/TCBB.2019.2957094"},{"key":"29_CR16","first-page":"233","volume":"8","author":"Z-A Huang","year":"2017","unstructured":"Huang, Z.-A., et al.: PBHMDA: path-based human microbe-disease association prediction. Front. Microbiol. 8, 233 (2017)","journal-title":"Front. Microbiol."},{"issue":"5","key":"29_CR17","doi-asserted-by":"publisher","first-page":"e1007568","DOI":"10.1371\/journal.pcbi.1007568","volume":"16","author":"L Wang","year":"2020","unstructured":"Wang, L., You, Z.-H., Li, Y.-M., Zheng, K., Huang, Y.-A.: GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm. PLoS Comput. Biol. 16(5), e1007568 (2020)","journal-title":"PLoS Comput. Biol."},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"758","DOI":"10.3389\/fgene.2019.00758","volume":"10","author":"Z-A Huang","year":"2019","unstructured":"Huang, Z.-A., et al.: Predicting lncRNA-miRNA interaction via graph convolution auto-encoder. Front. Genet. 10, 758 (2019)","journal-title":"Front. Genet."},{"issue":"3","key":"29_CR19","doi-asserted-by":"publisher","first-page":"e1005455","DOI":"10.1371\/journal.pcbi.1005455","volume":"13","author":"Z-H You","year":"2017","unstructured":"You, Z.-H., et al.: PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLoS Comput. Biol. 13(3), e1005455 (2017)","journal-title":"PLoS Comput. Biol."},{"issue":"23","key":"29_CR20","doi-asserted-by":"crossref","first-page":"3611","DOI":"10.1093\/bioinformatics\/btw498","volume":"32","author":"S Cogill","year":"2016","unstructured":"Cogill, S., Wang, L.: Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates. Bioinformatics 32(23), 3611\u20133618 (2016)","journal-title":"Bioinformatics"},{"issue":"10","key":"29_CR21","doi-asserted-by":"publisher","first-page":"6711","DOI":"10.1007\/s00521-018-3502-5","volume":"31","author":"M G\u00f6k","year":"2018","unstructured":"G\u00f6k, M.: A novel machine learning model to predict autism spectrum disorders risk gene. Neural Comput. Appl. 31(10), 6711\u20136717 (2018). https:\/\/doi.org\/10.1007\/s00521-018-3502-5","journal-title":"Neural Comput. Appl."},{"key":"29_CR22","unstructured":"Kou, Y., Betancur, C., Xu, H., Buxbaum, J.D., Ma\u2019Ayan, A.: Network-and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability. Am. J. Med. Genet. Part C: Seminar. Med. Genet. 160, 130\u2013142 (2012)"},{"issue":"3","key":"29_CR23","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1093\/bioinformatics\/btz621","volume":"36","author":"Y-A Huang","year":"2020","unstructured":"Huang, Y.-A., Hu, P., Chan, K.C., You, Z.-H.: Graph convolution for predicting associations between miRNA and drug resistance. Bioinformatics 36(3), 851\u2013858 (2020)","journal-title":"Bioinformatics"},{"issue":"50","key":"29_CR24","doi-asserted-by":"publisher","first-page":"87033","DOI":"10.18632\/oncotarget.18788","volume":"8","author":"Y-A Huang","year":"2017","unstructured":"Huang, Y.-A., et al.: EPMDA: an expression-profile based computational model for microRNA-disease association prediction. Oncotarget 8(50), 87033 (2017)","journal-title":"Oncotarget"},{"key":"29_CR25","doi-asserted-by":"publisher","unstructured":"Jiang, H.-J., Huang, Y.-A., You, Z.-H.: Predicting drug-disease associations via using gaussian interaction profile and kernel-based autoencoder. BioMed Res. Int. (2019). https:\/\/doi.org\/10.1155\/2019\/2426958","DOI":"10.1155\/2019\/2426958"},{"issue":"1","key":"29_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"H-J Jiang","year":"2020","unstructured":"Jiang, H.-J., Huang, Y.-A., You, Z.-H.: SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network. Sci. Rep. 10(1), 1\u201311 (2020)","journal-title":"Sci. Rep."},{"issue":"1","key":"29_CR27","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1186\/s12967-019-2127-5","volume":"17","author":"H-J Jiang","year":"2019","unstructured":"Jiang, H.-J., You, Z.-H., Huang, Y.-A.: Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks. J. Transl. Med. 17(1), 382 (2019)","journal-title":"J. Transl. Med."},{"issue":"9","key":"29_CR28","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1186\/s12918-018-0664-9","volume":"12","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Zhu, Z., You, Z.-H., Zeng, Z., Huang, Z.-A., Huang, Y.-A.: FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases. BMC Syst. Biol. 12(9), 121 (2018)","journal-title":"BMC Syst. Biol."},{"issue":"1","key":"29_CR29","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/2040-2392-3-12","volume":"3","author":"A Anitha","year":"2012","unstructured":"Anitha, A., et al.: Brain region-specific altered expression and association of mitochondria-related genes in autism. Mol. Autism 3(1), 12 (2012)","journal-title":"Mol. Autism"},{"issue":"1","key":"29_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-016-1111-6","volume":"15","author":"Y-A Huang","year":"2017","unstructured":"Huang, Y.-A., You, Z.-H., Chen, X., Huang, Z.-A., Zhang, S., Yan, G.-Y.: Prediction of microbe\u2013disease association from the integration of neighbor and graph with collaborative recommendation model. J. Transl. Med. 15(1), 1\u201311 (2017)","journal-title":"J. Transl. Med."},{"issue":"7416","key":"29_CR31","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1038\/nature11405","volume":"489","author":"MJ Hawrylycz","year":"2012","unstructured":"Hawrylycz, M.J., et al.: An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489(7416), 391 (2012)","journal-title":"Nature"},{"issue":"1","key":"29_CR32","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/2040-2392-4-36","volume":"4","author":"BS Abrahams","year":"2013","unstructured":"Abrahams, B.S., et al.: SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4(1), 36 (2013)","journal-title":"Mol. Autism"},{"issue":"7526","key":"29_CR33","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/nature13772","volume":"515","author":"S De Rubeis","year":"2014","unstructured":"De Rubeis, S., et al.: Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515(7526), 209\u2013215 (2014)","journal-title":"Nature"},{"issue":"D1","key":"29_CR34","doi-asserted-by":"publisher","first-page":"D1016","DOI":"10.1093\/nar\/gkr1145","volume":"40","author":"L-M Xu","year":"2012","unstructured":"Xu, L.-M., Li, J.-R., Huang, Y., Zhao, M., Tang, X., Wei, L.: AutismKB: an evidence-based knowledgebase of autism genetics. Nucleic Acids Res. 40(D1), D1016\u2013D1022 (2012)","journal-title":"Nucleic Acids Res."},{"issue":"3","key":"29_CR35","first-page":"265","volume":"88","author":"CE Lipscomb","year":"2000","unstructured":"Lipscomb, C.E.: Medical subject headings (MeSH). Bull. Med. Libr. Assoc. 88(3), 265\u2013266 (2000)","journal-title":"Bull. Med. Libr. Assoc."},{"key":"29_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878\u2013887. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11538059_91"},{"key":"29_CR37","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"29_CR38","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 (2014)"},{"key":"29_CR39","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"29_CR40","unstructured":"Brochu, E., Cora, V.M., De Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:10122599 (2010)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60802-6_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T17:37:20Z","timestamp":1696873040000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-60802-6_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030608019","9783030608026"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60802-6_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ic-ic.tongji.edu.cn\/2020\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}