{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:14:27Z","timestamp":1742915667210,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030849092"},{"type":"electronic","value":"9783030849108"}],"license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-84910-8_26","type":"book-chapter","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T03:51:48Z","timestamp":1628221908000},"page":"243-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis of Optical Mapping Data with Neural Network"],"prefix":"10.1007","author":[{"given":"V\u00edt","family":"Dole\u017e\u00ed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petr","family":"Gajdo\u0161","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"26_CR1","unstructured":"France G\u00e9nomique. Optical Mapping - France G\u00e9nomique (2021). https:\/\/www.france-genomique.org\/technological-expertises\/whole-genome\/optical-mapping\/?lang=en, Accessed 25 June 2021"},{"key":"26_CR2","doi-asserted-by":"publisher","unstructured":"Aston, C., Mishra, B., Schwartz, D.C.: Optical mapping and its potential for large-scale sequencing projects. Trends Biotechnol., s. 297\u2013302 (1999). https:\/\/doi.org\/10.1016\/S0167-7799(99)01326-8","DOI":"10.1016\/S0167-7799(99)01326-8"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chung, C.Y.L., Chan, T.F.: Advances in optical mapping for genomic research. Comput. Struct. Biotechnol. J. 18, 2051\u20132062 (2020)","DOI":"10.1016\/j.csbj.2020.07.018"},{"key":"26_CR4","unstructured":"Bionano Saphyr. https:\/\/bionanogenomics.com\/products\/saphyr\/, Accessed 24 June 2021"},{"key":"26_CR5","doi-asserted-by":"publisher","unstructured":"Jakobs, M., Dimitracopoulos, A., Franze, K.:. KymoButler, a deep learning software for automated kymograph analysis. eLife 8, e42288. https:\/\/doi.org\/10.7554\/elife.42288","DOI":"10.7554\/elife.42288"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"comma.ai - introducing openpilot. https:\/\/comma.ai\/, Accessed 24 June 2021","DOI":"10.3828\/coma.2021.25"},{"key":"26_CR7","unstructured":"Kite - Free AI Coding Assistant and Code Auto-Complete Plugin. https:\/\/www.kite.com\/, Accessed 24 June 2021"},{"issue":"3","key":"26_CR8","first-page":"17","volume":"3","author":"IM Nasser","year":"2019","unstructured":"Nasser, I.M., Abu-Naser, S.S.: Lung cancer detection using artificial neural network. Int. J. Eng. Inf. Syst. (IJEAIS) 3(3), 17\u201323 (2019)","journal-title":"Int. J. Eng. Inf. Syst. (IJEAIS)"},{"issue":"1","key":"26_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang, L., Lin, Z.Q., Wong, A.: Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci. Rep. 10(1), 1\u201312 (2020)","journal-title":"Sci. Rep."},{"key":"26_CR10","doi-asserted-by":"publisher","unstructured":"Shelton, J.M., et al.: Tools and pipelines for BioNano data: molecule assembly pipeline and FASTA super scaffolding tool. BMC Genom. 16(1) (2015). https:\/\/doi.org\/10.1186\/s12864-015-1911-8","DOI":"10.1186\/s12864-015-1911-8"},{"key":"26_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2020.07.018","author":"Y Yuan","year":"2020","unstructured":"Yuan, Y., Yik-Lok Chung, C., Chan, T.-F.: Advances in optical mapping for genomic research. Comput. Struct. Biotechnol. J. (2020). https:\/\/doi.org\/10.1016\/j.csbj.2020.07.018","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"10","key":"26_CR12","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1016\/s0031-3203(01)00178-9","volume":"35","author":"M Egmont-Petersen","year":"2002","unstructured":"Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural networks-a review. Pattern Recogn. 35(10), 2279\u20132301 (2002). https:\/\/doi.org\/10.1016\/s0031-3203(01)00178-9","journal-title":"Pattern Recogn."},{"issue":"12","key":"26_CR13","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1109\/12.106218","volume":"40","author":"CT Lin","year":"1991","unstructured":"Lin, C.T., Lee, C.S.G.: Neural-network-based fuzzy logic control and decision system. IEEE Trans. Comput. 40(12), 1320\u20131336 (1991)","journal-title":"IEEE Trans. Comput."},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"7","key":"26_CR15","first-page":"1281","volume":"34","author":"PF Alcantarilla","year":"2011","unstructured":"Alcantarilla, P.F., Solutions, T.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Patt. Anal. Mach. Intell 34(7), 1281\u20131298 (2011)","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell"},{"key":"26_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","volume-title":"Computer Vision \u2013 ECCV 2006","author":"H Bay","year":"2006","unstructured":"Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404\u2013417. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023_32"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150\u20131157. IEEE (1999)","DOI":"10.1109\/ICCV.1999.790410"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 International Conference on Computer Vision, pp. 2564\u20132571. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"26_CR19","unstructured":"NVIDIA V100 - TENSOR CORE GPU. https:\/\/images.nvidia.com\/content\/technologies\/volta\/pdf\/volta-v100-datasheet-update-us-1165301-r5.pdf, Accessed 24 June 2021"},{"key":"26_CR20","unstructured":"TensorFlow. https:\/\/www.tensorflow.org\/, Accessed 24 June 2021"},{"key":"26_CR21","unstructured":"Image Segmentation with Watershed Algorithm. https:\/\/docs.opencv.org\/4.5.2\/d3\/db4\/tutorial_py_watershed.html, Accessed 24 June 2021"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"26_CR23","doi-asserted-by":"publisher","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","volume":"7","author":"Yu Weng","year":"2019","unstructured":"Weng, Yu., Zhou, T., Li, Y., Qiu, X.: NAS-Unet: neural architecture search for medical image segmentation. IEEE Access 7, 44247\u201344257 (2019)","journal-title":"IEEE Access"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"26_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/978-3-030-32245-8_69","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"W Yan","year":"2019","unstructured":"Yan, W., et al.: The domain shift problem of medical image segmentation and vendor-adaptation by Unet-GAN. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 623\u2013631. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_69"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Chan, S., et al.: Structural variation detection and analysis using Bionano optical mapping (2018)","DOI":"10.1007\/978-1-4939-8666-8_16"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Bocklandt, S., Hastie, A., Cao, H.: Bionano genome mapping: high-throughput, ultra-long molecule genome analysis system for precision genome assembly and haploid-resolved structural variation discovery (2019)","DOI":"10.1007\/978-981-13-6037-4_7"}],"container-title":["Lecture Notes in Networks and Systems","Advances in Intelligent Networking and Collaborative Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-84910-8_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:13:37Z","timestamp":1699316017000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-84910-8_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,7]]},"ISBN":["9783030849092","9783030849108"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-84910-8_26","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,8,7]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"INCoS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Networking and Collaborative Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taichung","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"incos2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/incos\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}