{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:58:30Z","timestamp":1743101910047,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757618"},{"type":"electronic","value":"9783030757625"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-75762-5_14","type":"book-chapter","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T09:07:43Z","timestamp":1620464863000},"page":"164-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-training"],"prefix":"10.1007","author":[{"given":"Vineela","family":"Indla","sequence":"first","affiliation":[]},{"given":"Vennela","family":"Indla","sequence":"additional","affiliation":[]},{"given":"Sai","family":"Narayanan","sequence":"additional","affiliation":[]},{"given":"Akhilesh","family":"Ramachandran","sequence":"additional","affiliation":[]},{"given":"Arunkumar","family":"Bagavathi","sequence":"additional","affiliation":[]},{"given":"Vishalini Laguduva","family":"Ramnath","sequence":"additional","affiliation":[]},{"given":"Sathyanarayanan N.","family":"Aakur","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Aakur, S.N., Sarkar, S.: A perceptual prediction framework for self supervised event segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1197\u20131206 (2019)","DOI":"10.1109\/CVPR.2019.00129"},{"issue":"1","key":"14_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-14974-x","volume":"11","author":"H Ashoor","year":"2020","unstructured":"Ashoor, H., et al.: Graph embedding and unsupervised learning predict genomic sub-compartments from hic chromatin interaction data. Nat. Commun. 11(1), 1\u201311 (2020)","journal-title":"Nat. Commun."},{"key":"14_CR3","unstructured":"Baker, B., et al.: Emergent tool use from multi-agent autocurricula. arXiv preprint arXiv:1909.07528 (2019)"},{"issue":"1","key":"14_CR4","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1093\/bioinformatics\/btz541","volume":"36","author":"JM Bartoszewicz","year":"2020","unstructured":"Bartoszewicz, J.M., Seidel, A., Rentzsch, R., Renard, B.Y.: DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks. Bioinformatics 36(1), 81\u201389 (2020)","journal-title":"Bioinformatics"},{"issue":"7","key":"14_CR5","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1186\/s12859-018-2182-6","volume":"19","author":"A Fiannaca","year":"2018","unstructured":"Fiannaca, A., et al.: Deep learning models for bacteria taxonomic classification of metagenomic data. BMC Bioinform. 19(7), 198 (2018)","journal-title":"BMC Bioinform."},{"key":"14_CR6","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"14_CR9","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1093\/bioinformatics\/btr708","volume":"28","author":"W Huang","year":"2012","unstructured":"Huang, W., Li, L., Myers, J.R., Marth, G.T.: Art: a next-generation sequencing read simulator. Bioinformatics 28(4), 593\u2013594 (2012)","journal-title":"Bioinformatics"},{"issue":"D1","key":"14_CR10","doi-asserted-by":"publisher","first-page":"D573","DOI":"10.1093\/nar\/gky1126","volume":"47","author":"S Hwang","year":"2019","unstructured":"Hwang, S., Kim, C.Y., Yang, S., Kim, E., Hart, T., Marcotte, E.M., Lee, I.: Humannet v2: human gene networks for disease research. Nucleic Acids Res. 47(D1), D573\u2013D580 (2019)","journal-title":"Nucleic Acids Res."},{"issue":"4","key":"14_CR11","doi-asserted-by":"publisher","first-page":"6670","DOI":"10.1109\/LRA.2020.3013848","volume":"5","author":"A Kadian","year":"2020","unstructured":"Kadian, A., et al.: Sim2real predictivity: does evaluation in simulation predict real-world performance? IEEE Robot. Autom. Lett. 5(4), 6670\u20136677 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Kang, U., Tong, H., Sun, J.: Fast random walk graph kernel. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 828\u2013838. SIAM (2012)","DOI":"10.1137\/1.9781611972825.71"},{"key":"14_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bdq.2015.02.001","volume":"3","author":"T Laver","year":"2015","unstructured":"Laver, T., et al.: Assessing the performance of the oxford nanopore technologies minion. Biomol. Detect. Quantif. 3, 1\u20138 (2015)","journal-title":"Biomol. Detect. Quantif."},{"key":"14_CR14","unstructured":"Li, X., et al.: Online adaptation for consistent mesh reconstruction in the wild. In: Advances in Neural Information Processing Systems, 33 (2020)"},{"issue":"52","key":"14_CR15","doi-asserted-by":"publisher","first-page":"E8396","DOI":"10.1073\/pnas.1604560113","volume":"113","author":"Y Lin","year":"2016","unstructured":"Lin, Y., Yuan, J., Kolmogorov, M., Shen, M.W., Chaisson, M., Pevzner, P.A.: Assembly of long error-prone reads using de Bruijn graphs. Proc. Nat. Acad. Sci. 113(52), E8396\u2013E8405 (2016)","journal-title":"Proc. Nat. Acad. Sci."},{"key":"14_CR16","unstructured":"Lu, J., Yang, J., Batra, D., Parikh, D.: Hierarchical question-image co-attention for visual question answering. In: Advances in Neural Information Processing Systems, 29, pp. 289\u2013297 (2016)"},{"key":"14_CR17","unstructured":"Marzoev, A., Madden, S., Kaashoek, M.F., Cafarella, M., Andreas, J.: Unnatural language processing: bridging the gap between synthetic and natural language data. arXiv preprint arXiv:2004.13645 (2020)"},{"issue":"14","key":"14_CR18","doi-asserted-by":"publisher","first-page":"i92","DOI":"10.1093\/bioinformatics\/btx234","volume":"33","author":"X Min","year":"2017","unstructured":"Min, X., Zeng, W., Chen, N., Chen, T., Jiang, R.: Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding. Bioinform. 33(14), i92\u2013i101 (2017)","journal-title":"Bioinform."},{"key":"14_CR19","unstructured":"Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)"},{"key":"14_CR20","unstructured":"Narayanan, S., Ramachandran, A., Aakur, S.N., Bagavathi, A.: Genome sequence classification for animal diagnostics with graph representations and deep neural networks. arXiv preprint arXiv:2007.12791 (2020)"},{"key":"14_CR21","unstructured":"Nguyen, T.H., Chevaleyre, Y., Prifti, E., Sokolovska, N., Zucker, J.D.: Deep learning for metagenomic data: using 2D embeddings and convolutional neural networks. arXiv preprint arXiv:1712.00244 (2017)"},{"key":"14_CR22","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1093\/gbe\/evq004","volume":"2","author":"SC Perry","year":"2010","unstructured":"Perry, S.C., Beiko, R.G.: Distinguishing microbial genome fragments based on their composition: evolutionary and comparative genomic perspectives. Genome Biol. Evol. 2, 117\u2013131 (2010)","journal-title":"Genome Biol. Evol."},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Sadeghi, F., Toshev, A., Jang, E., Levine, S.: Sim2Real viewpoint invariant visual servoing by recurrent control. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (June 2018)","DOI":"10.1109\/CVPR.2018.00493"},{"issue":"1","key":"14_CR24","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/nar\/29.1.308","volume":"29","author":"ST Sherry","year":"2001","unstructured":"Sherry, S.T., et al.: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29(1), 308\u2013311 (2001)","journal-title":"Nucleic Acids Res."},{"issue":"3","key":"14_CR25","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.mimet.2013.07.002","volume":"94","author":"AH Stobbe","year":"2013","unstructured":"Stobbe, A.H., et al.: E-probe Diagnostic Nucleic acid Analysis (edna): a theoretical approach for handling of next generation sequencing data for diagnostics. J. Microbiol. Methods 94(3), 356\u2013366 (2013)","journal-title":"J. Microbiol. Methods"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75762-5_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:14:42Z","timestamp":1710350082000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75762-5_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757618","9783030757625"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75762-5_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"0","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":"23% - 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":"7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}