{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:20:24Z","timestamp":1743074424613,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030295622"},{"type":"electronic","value":"9783030295639"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29563-9_28","type":"book-chapter","created":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T11:03:53Z","timestamp":1566385433000},"page":"313-325","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature-Aware Attentive Convolutional Neural Network for Sequence Processing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-4540-0529","authenticated-orcid":false,"given":"Jingchao","family":"Dai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-0985-3790","authenticated-orcid":false,"given":"Kaiqi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuexiang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3220-904X","authenticated-orcid":false,"given":"Ying","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,22]]},"reference":[{"key":"28_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science (2014)"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Colloc, J., Yameogo, R.A., Summons, P.F., Shen, Y., Park, M., Aronson, J.E.: Epice an emotion fuzzy vectorial space for time modeling in medical decision. In: Proceedings of the 1st International Conference on Internet of Things and Machine Learning, p. 29. ACM (2017)","DOI":"10.1145\/3109761.3109790"},{"key":"28_CR3","unstructured":"Cui, Z., Chen, W., Chen, Y.: Multi-scale convolutional neural networks for time series classification (2016)"},{"issue":"7457","key":"28_CR4","first-page":"172","volume":"499","author":"R Debashish","year":"2015","unstructured":"Debashish, R., Hilal, K., Cook, K.B.: A compendium of RNA-binding motifs for decoding gene regulation. Nature 499(7457), 172\u2013177 (2015)","journal-title":"Nature"},{"key":"28_CR5","doi-asserted-by":"publisher","unstructured":"Dixit, P., Prajapati, G.I.: Machine learning in bioinformatics: a novel approach for DNA sequencing. In: Fifth International Conference on Advanced Computing Communication Technologies, pp. 41\u201347, February 2015. \n                      https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/ACCT.2015.73","DOI":"10.1109\/ACCT.2015.73"},{"issue":"Database issue","key":"28_CR6","first-page":"D180","volume":"40","author":"A Gerd","year":"2012","unstructured":"Gerd, A., Mackowiak, S.D., Jens, M.: doRiNA: a database of RNA interactions in post-transcriptional regulation. Nucleic Acids Res. 40(Database issue), D180\u2013D186 (2012)","journal-title":"Nucleic Acids Res."},{"issue":"11","key":"28_CR7","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). \n                      https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"28_CR8","unstructured":"Lei, K., et al.: Cooperative denoising for distantly supervised relation extraction. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 426\u2013436 (2018)"},{"key":"28_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-319-23063-4_21","volume-title":"Business Process Management","author":"A Leontjeva","year":"2015","unstructured":"Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297\u2013313. Springer, Cham (2015). \n                      https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-23063-4_21"},{"issue":"1","key":"28_CR10","doi-asserted-by":"publisher","first-page":"R17","DOI":"10.1186\/gb-2014-15-1-r17","volume":"15","author":"D Maticzka","year":"2014","unstructured":"Maticzka, D., Lange, S.J., Costa, F., Backofen, R.: GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol. 15(1), R17 (2014)","journal-title":"Genome Biol."},{"key":"28_CR11","unstructured":"Mnih, V., Heess, N., Graves, A., Kavukcuoglu, K.: Recurrent models of visual attention 3 (2014)"},{"key":"28_CR12","unstructured":"Nan, R.K., Zolna, K., Sordoni, A., Lin, Z., Pal, C.: Focused hierarchical RNNs for conditional sequence processing (2018)"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Pan, X., Shen, H.B.: Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks. Bioinformatics (2018)","DOI":"10.1093\/bioinformatics\/bty364"},{"issue":"1","key":"28_CR14","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1186\/s12864-016-2931-8","volume":"17","author":"X Pan","year":"2016","unstructured":"Pan, X., Fan, Y.X., Yan, J., Shen, H.B.: IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. BMC Genomics 17(1), 582 (2016)","journal-title":"BMC Genomics"},{"key":"28_CR15","unstructured":"Paulus, R., Xiong, C., Socher, R.: A deep reinforced model for abstractive summarization (2017)"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Shen, Y., et al.: Drug2Vec: knowledge-aware feature-driven method for drug representation learning. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 757\u2013800. IEEE (2018)","DOI":"10.1109\/BIBM.2018.8621390"},{"issue":"1","key":"28_CR17","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s13321-019-0342-y","volume":"11","author":"Y Shen","year":"2019","unstructured":"Shen, Y., et al.: KMR: knowledge-oriented medicine representation learning for drug-drug interaction and similarity computation. J. Cheminformatics 11(1), 22 (2019)","journal-title":"J. Cheminformatics"},{"key":"28_CR18","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks (2014)"},{"key":"28_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-319-99365-2_6","volume-title":"Knowledge Science, Engineering and Management","author":"Z Tian","year":"2018","unstructured":"Tian, Z., Rong, W., Shi, L., Liu, J., Xiong, Z.: Attention aware bidirectional gated recurrent unit based framework for sentiment analysis. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11061, pp. 67\u201378. Springer, Cham (2018). \n                      https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-99365-2_6"},{"key":"28_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need (2017)"},{"issue":"1","key":"28_CR21","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1882471.1882478","volume":"12","author":"Z Xing","year":"2010","unstructured":"Xing, Z., Pei, J., Keogh, E.J.: A brief survey on sequence classification. ACM SIGKDD Explor. Newsl. 12(1), 40\u201348 (2010)","journal-title":"ACM SIGKDD Explor. Newsl."},{"issue":"4","key":"28_CR22","doi-asserted-by":"publisher","first-page":"e32","DOI":"10.1093\/nar\/gkv1025","volume":"44","author":"S Zhang","year":"2015","unstructured":"Zhang, S., et al.: A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res. 44(4), e32 (2015)","journal-title":"Nucleic Acids Res."},{"key":"28_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-319-08010-9_33","volume-title":"Web-Age Information Management","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298\u2013310. Springer, Cham (2014). \n                      https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-08010-9_33"},{"issue":"10","key":"28_CR24","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"J Zhou","year":"2015","unstructured":"Zhou, J., Troyanskaya, O.G.: Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12(10), 931\u2013934 (2015)","journal-title":"Nat. Methods"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29563-9_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T06:07:52Z","timestamp":1569218872000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/10.1007\/978-3-030-29563-9_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030295622","9783030295639"],"references-count":24,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-030-29563-9_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.ksem.conferences.academy\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"240","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}