{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T15:28:57Z","timestamp":1772033337037,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T00:00:00Z","timestamp":1621555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,5,21]]},"DOI":"10.1145\/3473258.3473271","type":"proceedings-article","created":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T21:02:28Z","timestamp":1639256548000},"page":"81-89","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Extracting and Evaluating Features from RNA Virus Sequences to Predict Host Species Susceptibility Using Deep Learning"],"prefix":"10.1145","author":[{"given":"Kevin","family":"Sutanto","sequence":"first","affiliation":[{"name":"University of Ottawa, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"Turcotte","sequence":"additional","affiliation":[{"name":"University of Ottawa, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,12,11]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg\u00a0S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org.  Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg\u00a0S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org."},{"key":"e_1_3_2_2_2_1","volume-title":"Alignment-free Math 325 oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Research 45, 1 (11","author":"Ahlgren A","year":"2016","unstructured":"Nathan\u00a0 A Ahlgren , Jie Ren , Yang\u00a0Young Lu , Jed\u00a0 A Fuhrman , and Fengzhu Sun . 2016. Alignment-free Math 325 oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Research 45, 1 (11 2016 ), 39\u201353. https:\/\/doi.org\/10.1093\/nar\/gkw1002 arXiv:https:\/\/academic.oup.com\/nar\/article-pdf\/45\/1\/39\/29193437\/gkw1002.pdf 10.1093\/nar Nathan\u00a0A Ahlgren, Jie Ren, Yang\u00a0Young Lu, Jed\u00a0A Fuhrman, and Fengzhu Sun. 2016. Alignment-free Math 325 oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Research 45, 1 (11 2016), 39\u201353. https:\/\/doi.org\/10.1093\/nar\/gkw1002 arXiv:https:\/\/academic.oup.com\/nar\/article-pdf\/45\/1\/39\/29193437\/gkw1002.pdf"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gku1207"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/2370956.2370958"},{"key":"e_1_3_2_2_5_1","unstructured":"Fran\u00e7ois Chollet 2015. Keras. https:\/\/keras.io.  Fran\u00e7ois Chollet 2015. Keras. https:\/\/keras.io."},{"key":"e_1_3_2_2_6_1","volume-title":"Skip-mers: increasing entropy and sensitivity to detect conserved genic regions with simple cyclic q-grams. bioRxiv","author":"Clavijo J.","year":"2017","unstructured":"Bernardo\u00a0 J. Clavijo , Gonzalo\u00a0Garcia Accinelli , Luis Yanes , Katie Barr , and Jonathan Wright . 2017. Skip-mers: increasing entropy and sensitivity to detect conserved genic regions with simple cyclic q-grams. bioRxiv ( 2017 ). https:\/\/doi.org\/10.1101\/179960 10.1101\/179960 Bernardo\u00a0J. Clavijo, Gonzalo\u00a0Garcia Accinelli, Luis Yanes, Katie Barr, and Jonathan Wright. 2017. Skip-mers: increasing entropy and sensitivity to detect conserved genic regions with simple cyclic q-grams. bioRxiv (2017). https:\/\/doi.org\/10.1101\/179960"},{"key":"e_1_3_2_2_7_1","first-page":"1411","article-title":"Diseases of humans and their domestic mammals: pathogen characteristics, host range and the risk of emergence. Philosophical Transactions of the Royal Society of London","volume":"356","author":"Cleaveland S.","year":"2001","unstructured":"S. Cleaveland , M.K. Laurenson , and L.H. Taylor . 2001 . Diseases of humans and their domestic mammals: pathogen characteristics, host range and the risk of emergence. Philosophical Transactions of the Royal Society of London . Series B: Biological Sciences 356 , 1411 (July 2001), 991\u2013999. https:\/\/doi.org\/10.1098\/rstb.2001.0889 10.1098\/rstb.2001.0889 S. Cleaveland, M.K. Laurenson, and L.H. Taylor. 2001. Diseases of humans and their domestic mammals: pathogen characteristics, host range and the risk of emergence. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 356, 1411 (July 2001), 991\u2013999. https:\/\/doi.org\/10.1098\/rstb.2001.0889","journal-title":"Series B: Biological Sciences"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2008.0284"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/1755-8794-7-S3-S1"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1534\/genetics.106.064634"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-020-01533-w"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts565"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-009-9153-8"},{"key":"e_1_3_2_2_14_1","volume-title":"WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 33, 19 (07","author":"Galiez Clovis","year":"2017","unstructured":"Clovis Galiez , Matthias Siebert , Fran\u00e7ois Enault , Jonathan Vincent , and Johannes S\u00f6ding . 2017. WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 33, 19 (07 2017 ), 3113\u20133114. https:\/\/doi.org\/10.1093\/bioinformatics\/btx383 arXiv:https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/19\/3113\/25164881\/btx383.pdf 10.1093\/bioinformatics Clovis Galiez, Matthias Siebert, Fran\u00e7ois Enault, Jonathan Vincent, and Johannes S\u00f6ding. 2017. WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 33, 19 (07 2017), 3113\u20133114. https:\/\/doi.org\/10.1093\/bioinformatics\/btx383 arXiv:https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/19\/3113\/25164881\/btx383.pdf"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/17130"},{"key":"e_1_3_2_2_16_1","volume-title":"On the antisymmetry of the amino acid code table. Origins of life 10, 3","author":"Hasegawa Masami","year":"1980","unstructured":"Masami Hasegawa and Takashi Miyata . 1980. On the antisymmetry of the amino acid code table. Origins of life 10, 3 ( 1980 ), 265\u2013270. Masami Hasegawa and Takashi Miyata. 1980. On the antisymmetry of the amino acid code table. Origins of life 10, 3 (1980), 265\u2013270."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0097-8485(99)00013-3"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1248465"},{"key":"e_1_3_2_2_19_1","volume-title":"Kingma and Jimmy Ba","author":"P.","year":"2017","unstructured":"Diederik\u00a0 P. Kingma and Jimmy Ba . 2017 . Adam : A Method for Stochastic Optimization . arxiv:1412.6980\u00a0[cs.LG] Diederik\u00a0P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arxiv:1412.6980\u00a0[cs.LG]"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-018-0207-8"},{"key":"e_1_3_2_2_21_1","first-page":"1","article-title":"Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences","volume":"8","author":"Li Han","year":"2018","unstructured":"Han Li and Fengzhu Sun . 2018 . Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences . Scientific Reports 8 , 1 (July 2018), 10032. https:\/\/doi.org\/10.1038\/s41598-018-28308-x 10.1038\/s41598-018-28308-x Han Li and Fengzhu Sun. 2018. Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences. Scientific Reports 8, 1 (July 2018), 10032. https:\/\/doi.org\/10.1038\/s41598-018-28308-x","journal-title":"Scientific Reports"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl158"},{"key":"e_1_3_2_2_23_1","volume-title":"Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 166 (Aug","author":"Li Yu","year":"2019","unstructured":"Yu Li , Chao Huang , Lizhong Ding , Zhongxiao Li , Yijie Pan , and Xin Gao . 2019. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 166 (Aug . 2019 ), 4\u201321. https:\/\/doi.org\/10.1016\/j.ymeth.2019.04.008 10.1016\/j.ymeth.2019.04.008 Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li, Yijie Pan, and Xin Gao. 2019. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 166 (Aug. 2019), 4\u201321. https:\/\/doi.org\/10.1016\/j.ymeth.2019.04.008"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2015.08.025"},{"key":"e_1_3_2_2_25_1","volume-title":"Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?PLOS Pathogens 16, 5 (May","author":"Liu Ping","year":"2020","unstructured":"Ping Liu , Jing-Zhe Jiang , Xiu-Feng Wan , Yan Hua , Linmiao Li , Jiabin Zhou , Xiaohu Wang , Fanghui Hou , Jing Chen , Jiejian Zou , and Jinping Chen . 2020. Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?PLOS Pathogens 16, 5 (May 2020 ), e1008421. https:\/\/doi.org\/10.1371\/journal.ppat.1008421 10.1371\/journal.ppat.1008421 Ping Liu, Jing-Zhe Jiang, Xiu-Feng Wan, Yan Hua, Linmiao Li, Jiabin Zhou, Xiaohu Wang, Fanghui Hou, Jing Chen, Jiejian Zou, and Jinping Chen. 2020. Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?PLOS Pathogens 16, 5 (May 2020), e1008421. https:\/\/doi.org\/10.1371\/journal.ppat.1008421"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1004395"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/29.22.4724"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.coviro.2013.02.002"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Ilaria Manfredonia Chandran Nithin Almudena Ponce-Salvatierra Pritha Ghosh Tomasz\u00a0K Wirecki Tycho Marinus Natacha\u00a0S Ogando Eric\u00a0J Snijder Martijn\u00a0J van Hemert Janusz\u00a0M Bujnicki 2020. Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements. Nucleic acids research(2020).  Ilaria Manfredonia Chandran Nithin Almudena Ponce-Salvatierra Pritha Ghosh Tomasz\u00a0K Wirecki Tycho Marinus Natacha\u00a0S Ogando Eric\u00a0J Snijder Martijn\u00a0J van Hemert Janusz\u00a0M Bujnicki 2020. Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements. Nucleic acids research(2020).","DOI":"10.1093\/nar\/gkaa1053"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.22354\/in.v24i3.848"},{"key":"e_1_3_2_2_31_1","volume-title":"viral host prediction with Deep Learning. Bioinformatics (08","author":"Mock Florian","year":"2020","unstructured":"Florian Mock , Adrian Viehweger , Emanuel Barth , and Manja Marz . 2020. VIDHOP , viral host prediction with Deep Learning. Bioinformatics (08 2020 ). https:\/\/doi.org\/10.1093\/bioinformatics\/btaa705 arXiv:https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa705\/33637195\/btaa705.pdfbtaa705. 10.1093\/bioinformatics Florian Mock, Adrian Viehweger, Emanuel Barth, and Manja Marz. 2020. VIDHOP, viral host prediction with Deep Learning. Bioinformatics (08 2020). https:\/\/doi.org\/10.1093\/bioinformatics\/btaa705 arXiv:https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa705\/33637195\/btaa705.pdfbtaa705."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1919176117"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1128\/MCB.20.23.8635-8642.2000"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature22975"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty364"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1261\/rna.076141.120"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40168-017-0283-5"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40484-019-0187-4"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1128\/JVI.73.7.5787-5794.1999"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM49941.2020.9313183"},{"key":"e_1_3_2_2_43_1","first-page":"1411","article-title":"Risk factors for human disease emergence. Philosophical Transactions of the Royal Society of London","volume":"356","author":"Taylor H.","year":"2001","unstructured":"Louise\u00a0 H. Taylor , Sophia\u00a0 M. Latham , and Mark\u00a0 E.J. Woolhouse . 2001 . Risk factors for human disease emergence. Philosophical Transactions of the Royal Society of London . Series B: Biological Sciences 356 , 1411 (July 2001), 983\u2013989. https:\/\/doi.org\/10.1098\/rstb.2001.0888 10.1098\/rstb.2001.0888 Louise\u00a0H. Taylor, Sophia\u00a0M. Latham, and Mark\u00a0E.J. Woolhouse. 2001. Risk factors for human disease emergence. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 356, 1411 (July 2001), 983\u2013989. https:\/\/doi.org\/10.1098\/rstb.2001.0888","journal-title":"Series B: Biological Sciences"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1004902"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1080\/10408410701647560"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tree.2005.02.009"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygeno.2015.12.002"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1473-7"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2020.03.063"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.2174\/1389200219666180829121038"}],"event":{"name":"ICBBT '21: 2021 13th International Conference on Bioinformatics and Biomedical Technology","location":"Xi'an China","acronym":"ICBBT '21"},"container-title":["2021 13th International Conference on Bioinformatics and Biomedical Technology"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3473258.3473271","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3473258.3473271","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:15Z","timestamp":1750195695000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3473258.3473271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,21]]},"references-count":50,"alternative-id":["10.1145\/3473258.3473271","10.1145\/3473258"],"URL":"https:\/\/doi.org\/10.1145\/3473258.3473271","relation":{},"subject":[],"published":{"date-parts":[[2021,5,21]]},"assertion":[{"value":"2021-12-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}