{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T19:37:57Z","timestamp":1775763477580,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-021-00360-9","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T16:02:59Z","timestamp":1624896179000},"page":"556-565","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":220,"title":["Machine learning and computation-enabled intelligent sensor design"],"prefix":"10.1038","volume":"3","author":[{"given":"Zachary","family":"Ballard","sequence":"first","affiliation":[]},{"given":"Calvin","family":"Brown","sequence":"additional","affiliation":[]},{"given":"Asad M.","family":"Madni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0717-683X","authenticated-orcid":false,"given":"Aydogan","family":"Ozcan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"360_CR1","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/nrg3920","volume":"16","author":"MW Libbrecht","year":"2015","unstructured":"Libbrecht, M. W. & Noble, W. S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321\u2013332 (2015).","journal-title":"Nat. Rev. Genet."},{"key":"360_CR2","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1039\/C5SC02632F","volume":"7","author":"JR Askim","year":"2016","unstructured":"Askim, J. R., Li, Z., LaGasse, M. K., Rankin, J. M. & Suslick, K. S. An optoelectronic nose for identification of explosives. Chem. Sci. 7, 199\u2013206 (2016).","journal-title":"Chem. Sci."},{"key":"360_CR3","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1038\/nature26000","volume":"555","author":"D Capper","year":"2018","unstructured":"Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469\u2013474 (2018).","journal-title":"Nature"},{"key":"360_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41378-020-0161-3","volume":"6","author":"T Hayasaka","year":"2020","unstructured":"Hayasaka, T. et al. An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol. Microsyst. Nanoeng. 6, 1\u201313 (2020).","journal-title":"Microsyst. Nanoeng."},{"key":"360_CR5","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","volume":"7","author":"Y Chen","year":"2014","unstructured":"Chen, Y., Lin, Z., Zhao, X., Wang, G. & Gu, Y. Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 2094\u20132107 (2014).","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"360_CR6","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1364\/OPTICA.4.001437","volume":"4","author":"Y Rivenson","year":"2017","unstructured":"Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437\u20131443 (2017).","journal-title":"Optica"},{"key":"360_CR7","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1364\/OPTICA.5.000960","volume":"5","author":"N Borhani","year":"2018","unstructured":"Borhani, N., Kakkava, E., Moser, C. & Psaltis, D. Learning to see through multimode fibers. Optica 5, 960\u2013966 (2018).","journal-title":"Optica"},{"key":"360_CR8","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1038\/s42256-020-0199-9","volume":"2","author":"B Rahmani","year":"2020","unstructured":"Rahmani, B. et al. Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media. Nat. Mach. Intell. 2, 403\u2013410 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"360_CR9","doi-asserted-by":"publisher","first-page":"6529","DOI":"10.1021\/acs.analchem.0c00137","volume":"92","author":"S-Y Cho","year":"2020","unstructured":"Cho, S.-Y. et al. Finding hidden signals in chemical sensors using deep learning. Anal. Chem. 92, 6529\u20136537 (2020).","journal-title":"Anal. Chem."},{"key":"360_CR10","doi-asserted-by":"publisher","first-page":"2527","DOI":"10.1021\/acsphotonics.0c00841","volume":"7","author":"C Brown","year":"2020","unstructured":"Brown, C. et al. Automated, cost-effective optical system for accelerated antimicrobial susceptibility testing (AST) using deep learning. ACS Photon. 7, 2527\u20132538 (2020).","journal-title":"ACS Photon."},{"key":"360_CR11","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1038\/s41746-020-0282-y","volume":"3","author":"K de Haan","year":"2020","unstructured":"de Haan, K. et al. Automated screening of sickle cells using a smartphone-based microscope and deep learning. npj Digit. Med. 3, 76 (2020).","journal-title":"npj Digit. Med."},{"key":"360_CR12","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s41377-018-0067-0","volume":"7","author":"Z G\u04e7r\u04e7cs","year":"2018","unstructured":"G\u04e7r\u04e7cs, Z. et al. A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples. Light Sci. Appl. 7, 66 (2018).","journal-title":"Light Sci. Appl."},{"key":"360_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-09103-2","volume":"10","author":"L Li","year":"2019","unstructured":"Li, L. et al. Machine-learning reprogrammable metasurface imager. Nat. Commun. 10, 1082 (2019).","journal-title":"Nat. Commun."},{"key":"360_CR14","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1038\/s41566-018-0300-7","volume":"13","author":"MP Edgar","year":"2019","unstructured":"Edgar, M. P., Gibson, G. M. & Padgett, M. J. Principles and prospects for single-pixel imaging. Nat. Photon. 13, 13\u201320 (2019).","journal-title":"Nat. Photon."},{"key":"360_CR15","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1038\/s41377-019-0223-1","volume":"8","author":"Y Luo","year":"2019","unstructured":"Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl. 8, 112 (2019).","journal-title":"Light Sci. Appl."},{"key":"360_CR16","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1002\/cpa.20124","volume":"59","author":"EJ Cand\u00e8s","year":"2006","unstructured":"Cand\u00e8s, E. J., Romberg, J. K. & Tao, T. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207\u20131223 (2006).","journal-title":"Commun. Pure Appl. Math."},{"key":"360_CR17","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1137\/080725891","volume":"2","author":"T Goldstein","year":"2009","unstructured":"Goldstein, T. & Osher, S. The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2, 323\u2013343 (2009).","journal-title":"SIAM J. Imaging Sci."},{"key":"360_CR18","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2007.914730","volume":"25","author":"MF Duarte","year":"2008","unstructured":"Duarte, M. F. et al. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25, 83\u201391 (2008).","journal-title":"IEEE Signal Process. Mag."},{"key":"360_CR19","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1126\/science.aax8814","volume":"365","author":"Z Yang","year":"2019","unstructured":"Yang, Z. et al. Single-nanowire spectrometers. Science 365, 1017\u20131020 (2019).","journal-title":"Science"},{"key":"360_CR20","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/nature14576","volume":"523","author":"J Bao","year":"2015","unstructured":"Bao, J. & Bawendi, M. G. A colloidal quantum dot spectrometer. Nature 523, 67\u201370 (2015).","journal-title":"Nature"},{"key":"360_CR21","doi-asserted-by":"publisher","first-page":"25608","DOI":"10.1364\/OE.22.025608","volume":"22","author":"Z Wang","year":"2014","unstructured":"Wang, Z. & Yu, Z. Spectral analysis based on compressive sensing in nanophotonic structures. Opt. Express 22, 25608\u201325614 (2014).","journal-title":"Opt. Express"},{"key":"360_CR22","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1109\/TCI.2018.2864659","volume":"4","author":"K Degraux","year":"2018","unstructured":"Degraux, K., Cambareri, V., Geelen, B., Jacques, L. & Lafruit, G. Multispectral compressive imaging strategies using Fabry\u2013P\u00e9rot filtered sensors. IEEE Trans. Comput. Imaging 4, 661\u2013673 (2018).","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"360_CR23","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.1364\/OL.42.001820","volume":"42","author":"R French","year":"2017","unstructured":"French, R., Gigan, S. & Muskens, O. L. Speckle-based hyperspectral imaging combining multiple scattering and compressive sensing in nanowire mats. Opt. Lett. 42, 1820\u20131823 (2017).","journal-title":"Opt. Lett."},{"key":"360_CR24","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1364\/OL.42.000025","volume":"42","author":"Y Oiknine","year":"2017","unstructured":"Oiknine, Y., August, I., Blumberg, D. G. & Stern, A. Compressive sensing resonator spectroscopy. Opt. Lett. 42, 25\u201328 (2017).","journal-title":"Opt. Lett."},{"key":"360_CR25","doi-asserted-by":"publisher","first-page":"4996","DOI":"10.1364\/OL.38.004996","volume":"38","author":"Y August","year":"2013","unstructured":"August, Y. & Stern, A. Compressive sensing spectrometry based on liquid crystal devices. Opt. Lett. 38, 4996\u20134999 (2013).","journal-title":"Opt. Lett."},{"key":"360_CR26","doi-asserted-by":"publisher","first-page":"081103","DOI":"10.1063\/1.5143114","volume":"116","author":"T Sarwar","year":"2020","unstructured":"Sarwar, T., Cheekati, S., Chung, K. & Ku, P.-C. On-chip optical spectrometer based on GaN wavelength-selective nanostructural absorbers. Appl. Phys. Lett. 116, 081103 (2020).","journal-title":"Appl. Phys. Lett."},{"key":"360_CR27","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1109\/LPT.2020.2970742","volume":"32","author":"G Zhou","year":"2020","unstructured":"Zhou, G., Qi, Y., Lim, Z. H. & Zhou, G. Single-pixel MEMS spectrometer based on compressive sensing. IEEE Photonics Technol. Lett. 32, 287\u2013290 (2020).","journal-title":"IEEE Photonics Technol. Lett."},{"key":"360_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-06773-2","volume":"9","author":"DM Kita","year":"2018","unstructured":"Kita, D. M. et al. High-performance and scalable on-chip digital Fourier transform spectroscopy. Nat. Commun. 9, 4405 (2018).","journal-title":"Nat. Commun."},{"key":"360_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-08994-5","volume":"10","author":"Z Wang","year":"2019","unstructured":"Wang, Z. et al. Single-shot on-chip spectral sensors based on photonic crystal slabs. Nat. Commun. 10, 1020 (2019).","journal-title":"Nat. Commun."},{"key":"360_CR30","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1038\/s41566-019-0394-6","volume":"13","author":"F Yesilkoy","year":"2019","unstructured":"Yesilkoy, F. et al. Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces. Nat. Photon. 13, 390\u2013396 (2019).","journal-title":"Nat. Photon."},{"key":"360_CR31","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15950-1","volume":"11","author":"T Jiang","year":"2020","unstructured":"Jiang, T., Li, C., He, Q. & Peng, Z.-K. Randomized resonant metamaterials for single-sensor identification of elastic vibrations. Nat. Commun. 11, 2353 (2020).","journal-title":"Nat. Commun."},{"key":"360_CR32","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1109\/TMC.2011.216","volume":"11","author":"C Feng","year":"2012","unstructured":"Feng, C., Au, W. S. A., Valaee, S. & Tan, Z. Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans. Mob. Comput. 11, 1983\u20131993 (2012).","journal-title":"IEEE Trans. Mob. Comput."},{"key":"360_CR33","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.optcom.2018.05.046","volume":"426","author":"X Zhang","year":"2018","unstructured":"Zhang, X. et al. MEMS-based super-resolution remote sensing system using compressive sensing. Opt. Commun. 426, 410\u2013417 (2018).","journal-title":"Opt. Commun."},{"key":"360_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0526-1","volume":"40","author":"Y Wang","year":"2016","unstructured":"Wang, Y., Doleschel, S., Wunderlich, R. & Heinen, S. Evaluation of digital compressed sensing for real-time wireless ECG system with Bluetooth Low Energy. J. Med. Syst. 40, 170 (2016).","journal-title":"J. Med. Syst."},{"key":"360_CR35","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.dsp.2016.10.006","volume":"60","author":"H Djelouat","year":"2017","unstructured":"Djelouat, H., Ait Si Ali, A., Amira, A. & Bensaali, F. Compressive sensing based electronic nose platform. Digit. Signal Process. 60, 350\u2013359 (2017).","journal-title":"Digit. Signal Process."},{"key":"360_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18471-z","volume":"11","author":"Q Shi","year":"2020","unstructured":"Shi, Q. et al. Deep learning enabled smart mats as a scalable floor monitoring system. Nat. Commun. 11, 4609 (2020).","journal-title":"Nat. Commun."},{"key":"360_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15086-2","volume":"11","author":"N Golestani","year":"2020","unstructured":"Golestani, N. & Moghaddam, M. Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks. Nat. Commun. 11, 1551 (2020).","journal-title":"Nat. Commun."},{"key":"360_CR38","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1038\/nphoton.2015.69","volume":"9","author":"AY Piggott","year":"2015","unstructured":"Piggott, A. Y. et al. Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer. Nat. Photon. 9, 374\u2013377 (2015).","journal-title":"Nat. Photon."},{"key":"360_CR39","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/s41566-018-0246-9","volume":"12","author":"S Molesky","year":"2018","unstructured":"Molesky, S. et al. Inverse design in nanophotonics. Nat. Photon. 12, 659\u2013670 (2018).","journal-title":"Nat. Photon."},{"key":"360_CR40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-01939-2","volume":"7","author":"AY Piggott","year":"2017","unstructured":"Piggott, A. Y., Petykiewicz, J., Su, L. & Vu\u010dkovi\u0107, J. Fabrication-constrained nanophotonic inverse design. Sci. Rep. 7, 1786 (2017).","journal-title":"Sci. Rep."},{"key":"360_CR41","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1021\/acsnano.9b08151","volume":"14","author":"H-A Joung","year":"2020","unstructured":"Joung, H.-A. et al. Point-of-care serodiagnostic test for early-stage Lyme disease using a multiplexed paper-based immunoassay and machine learning. ACS Nano 14, 229\u2013240 (2020).","journal-title":"ACS Nano"},{"key":"360_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18677-1","volume":"11","author":"NM Angenent-Mari","year":"2020","unstructured":"Angenent-Mari, N. M., Garruss, A. S., Soenksen, L. R., Church, G. & Collins, J. J. A deep learning approach to programmable RNA switches. Nat. Commun. 11, 5057 (2020).","journal-title":"Nat. Commun."},{"key":"360_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18676-2","volume":"11","author":"JA Valeri","year":"2020","unstructured":"Valeri, J. A. et al. Sequence-to-function deep learning frameworks for engineered riboregulators. Nat. Commun. 11, 5058 (2020).","journal-title":"Nat. Commun."},{"key":"360_CR44","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T. M. & Wang, D. A survey of transfer learning. J. Big Data 3, 9 (2016).","journal-title":"J. Big Data"},{"key":"360_CR45","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s10115-013-0665-3","volume":"36","author":"D Cook","year":"2013","unstructured":"Cook, D., Feuz, K. D. & Krishnan, N. C. Transfer learning for activity recognition: a survey. Knowl. Inf. Syst. 36, 537\u2013556 (2013).","journal-title":"Knowl. Inf. Syst."},{"key":"360_CR46","doi-asserted-by":"publisher","unstructured":"Saeedi, R., Ghasemzadeh, H. & Gebremedhin, A. H. Transfer learning algorithms for autonomous reconfiguration of wearable systems. In 2016 IEEE International Conference on Big Data (Big Data) 563\u2013569 (IEEE, 2016); https:\/\/doi.org\/10.1109\/BigData.2016.7840648","DOI":"10.1109\/BigData.2016.7840648"},{"key":"360_CR47","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys, Y., Inza, I. & Larra\u00f1aga, P. A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507\u20132517 (2007).","journal-title":"Bioinformatics"},{"key":"360_CR48","doi-asserted-by":"publisher","first-page":"7434","DOI":"10.1021\/acsnano.8b04726","volume":"12","author":"B Cao","year":"2018","unstructured":"Cao, B. et al. How To optimize materials and devices via design of experiments and machine learning: demonstration using organic photovoltaics. ACS Nano 12, 7434\u20137444 (2018).","journal-title":"ACS Nano"},{"key":"360_CR49","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.trechm.2020.12.001","volume":"3","author":"NS Eyke","year":"2021","unstructured":"Eyke, N. S., Koscher, B. A. & Jensen, K. F. Toward machine learning-enhanced high-throughput experimentation. Trends Chem. 3, 120\u2013132 (2021).","journal-title":"Trends Chem."},{"key":"360_CR50","doi-asserted-by":"publisher","first-page":"10828","DOI":"10.1364\/OE.25.010828","volume":"25","author":"T Feichtner","year":"2017","unstructured":"Feichtner, T., Selig, O. & Hecht, B. Plasmonic nanoantenna design and fabrication based on evolutionary optimization. Opt. Express 25, 10828\u201310842 (2017).","journal-title":"Opt. Express"},{"key":"360_CR51","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.sbi.2019.01.013","volume":"57","author":"JA Kaczmarski","year":"2019","unstructured":"Kaczmarski, J. A., Mitchell, J. A., Spence, M. A., Vongsouthi, V. & Jackson, C. J. Structural and evolutionary approaches to the design and optimization of fluorescence-based small molecule biosensors. Curr. Opin. Struct. Biol. 57, 31\u201338 (2019).","journal-title":"Curr. Opin. Struct. Biol."},{"key":"360_CR52","doi-asserted-by":"publisher","first-page":"2266","DOI":"10.1021\/acsnano.7b00105","volume":"11","author":"ZS Ballard","year":"2017","unstructured":"Ballard, Z. S. et al. Computational sensing using low-cost and mobile plasmonic readers designed by machine learning. ACS Nano 11, 2266\u20132274 (2017).","journal-title":"ACS Nano"},{"key":"360_CR53","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1039\/C4LC00010B","volume":"14","author":"A Ozcan","year":"2014","unstructured":"Ozcan, A. Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools. Lab Chip 14, 3187\u20133194 (2014).","journal-title":"Lab Chip"},{"key":"360_CR54","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.ijar.2013.04.003","volume":"55","author":"F Min","year":"2014","unstructured":"Min, F., Hu, Q. & Zhu, W. Feature selection with test cost constraint. Int. J. Approx. Reason. 55, 167\u2013179 (2014).","journal-title":"Int. J. Approx. Reason."},{"key":"360_CR55","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1109\/TMC.2014.2331969","volume":"14","author":"H Ghasemzadeh","year":"2015","unstructured":"Ghasemzadeh, H., Amini, N., Saeedi, R. & Sarrafzadeh, M. Power-aware computing in wearable sensor networks: an optimal feature selection. IEEE Trans. Mob. Comput. 14, 800\u2013812 (2015).","journal-title":"IEEE Trans. Mob. Comput."},{"key":"360_CR56","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1038\/s41586-020-2917-1","volume":"587","author":"BS Miller","year":"2020","unstructured":"Miller, B. S. et al. Spin-enhanced nanodiamond biosensing for ultrasensitive diagnostics. Nature 587, 588\u2013593 (2020).","journal-title":"Nature"},{"key":"360_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-0274-y","volume":"3","author":"ZS Ballard","year":"2020","unstructured":"Ballard, Z. S. et al. Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors. npj Digit. Med. 3, 1\u20138 (2020).","journal-title":"npj Digit. Med."},{"key":"360_CR58","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1038\/s41587-020-0659-0","volume":"38","author":"JD Whitman","year":"2020","unstructured":"Whitman, J. D. et al. Evaluation of SARS-CoV-2 serology assays reveals a range of test performance. Nat. Biotechnol. 38, 1174\u20131183 (2020).","journal-title":"Nat. Biotechnol."},{"key":"360_CR59","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1002\/jmv.25727","volume":"92","author":"Z Li","year":"2020","unstructured":"Li, Z. et al. Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-2 infection diagnosis. J. Med. Virol. 92, 1518\u20131524 (2020).","journal-title":"J. Med. Virol."},{"key":"360_CR60","doi-asserted-by":"publisher","unstructured":"Espejo, A. P. et al. Review of current advances in serologic testing for COVID-19. Am. J. Clin. Pathol. https:\/\/doi.org\/10.1093\/ajcp\/aqaa112 (2020).","DOI":"10.1093\/ajcp\/aqaa112"},{"key":"360_CR61","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1038\/s41591-020-0913-5","volume":"26","author":"F Amanat","year":"2020","unstructured":"Amanat, F. et al. A serological assay to detect SARS-CoV-2 seroconversion in humans. Nat. Med. 26, 1033\u20131036 (2020).","journal-title":"Nat. Med."},{"key":"360_CR62","doi-asserted-by":"publisher","first-page":"104572","DOI":"10.1016\/j.jcv.2020.104572","volume":"130","author":"M Johnson","year":"2020","unstructured":"Johnson, M. et al. Evaluation of a novel multiplexed assay for determining IgG levels and functional activity to SARS-CoV-2. J. Clin. Virol. 130, 104572 (2020).","journal-title":"J. Clin. Virol."},{"key":"360_CR63","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.1093\/cid\/ciaa460","volume":"71","author":"AT Xiao","year":"2020","unstructured":"Xiao, A. T., Tong, Y. X. & Zhang, S. Profile of RT-PCR for SARS-CoV-2: a preliminary study from 56 COVID-19 patients. Clin. Infect. Dis. 71, 2249\u20132251 (2020).","journal-title":"Clin. Infect. Dis."},{"key":"360_CR64","doi-asserted-by":"publisher","first-page":"e00310","DOI":"10.1128\/JCM.00310-20","volume":"58","author":"JF-W Chan","year":"2020","unstructured":"Chan, J. F.-W. et al. Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp\/Hel real-time reverse transcription-PCR assay validated in vitro and with clinical specimens. J. Clin. Microbiol. 58, e00310\u2013e00320 (2020).","journal-title":"J. Clin. Microbiol."},{"key":"360_CR65","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-017-0493-2","volume":"90","author":"KP Soh","year":"2017","unstructured":"Soh, K. P., Szczurek, E., Sakoparnig, T. & Beerenwinkel, N. Predicting cancer type from tumour DNA signatures. Genome Med. 90, 104 (2017).","journal-title":"Genome Med."},{"key":"360_CR66","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","volume":"173","author":"DM Camacho","year":"2018","unstructured":"Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C. & Collins, J. J. Next-generation machine learning for biological networks. Cell 173, 1581\u20131592 (2018).","journal-title":"Cell"},{"key":"360_CR67","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41588-018-0295-5","volume":"51","author":"J Zou","year":"2019","unstructured":"Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12\u201318 (2019).","journal-title":"Nat. Genet."},{"key":"360_CR68","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","volume":"20","author":"G Eraslan","year":"2019","unstructured":"Eraslan, G., Avsec, \u017d., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389\u2013403 (2019).","journal-title":"Nat. Rev. Genet."},{"key":"360_CR69","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-019-1727-y","volume":"20","author":"RR Wick","year":"2019","unstructured":"Wick, R. R., Judd, L. M. & Holt, K. E. Performance of neural network basecalling tools for Oxford nanopore sequencing. Genome Biol. 20, 129 (2019).","journal-title":"Genome Biol."},{"key":"360_CR70","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giy037","volume":"7","author":"H Teng","year":"2018","unstructured":"Teng, H. et al. Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience 7, giy037 (2018).","journal-title":"GigaScience"},{"key":"360_CR71","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2009-10-8-r83","volume":"10","author":"M Kircher","year":"2009","unstructured":"Kircher, M., Stenzel, U. & Kelso, J. Improved base calling for the Illumina Genome Analyzer using machine learning strategies. Genome Biol. 10, R83 (2009).","journal-title":"Genome Biol."},{"key":"360_CR72","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1016\/j.cell.2016.04.059","volume":"165","author":"K Pardee","year":"2016","unstructured":"Pardee, K. et al. Rapid, low-cost detection of Zika virus using programmable biomolecular components. Cell 165, 1255\u20131266 (2016).","journal-title":"Cell"},{"key":"360_CR73","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267\u2013288 (1996).","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"360_CR74","doi-asserted-by":"publisher","DOI":"10.1038\/srep13169","volume":"5","author":"A Kumar Myakalwar","year":"2015","unstructured":"Kumar Myakalwar, A. et al. Less is more: avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection. Sci. Rep. 5, 13169 (2015).","journal-title":"Sci. Rep."},{"key":"360_CR75","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.aca.2010.03.048","volume":"667","author":"Z Xiaobo","year":"2010","unstructured":"Xiaobo, Z., Jiewen, Z., Povey, M. J. W., Holmes, M. & Hanpin, M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 667, 14\u201332 (2010).","journal-title":"Anal. Chim. Acta"},{"key":"360_CR76","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.aca.2019.07.012","volume":"1080","author":"C Yan","year":"2019","unstructured":"Yan, C. et al. A novel hybrid feature selection strategy in quantitative analysis of laser-induced breakdown spectroscopy. Anal. Chim. Acta 1080, 35\u201342 (2019).","journal-title":"Anal. Chim. Acta"},{"key":"360_CR77","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1002\/dta.2138","volume":"9","author":"MJ Anzanello","year":"2017","unstructured":"Anzanello, M. J. et al. A genetic algorithm-based framework for wavelength selection on sample categorization. Drug Test. Anal. 9, 1172\u20131181 (2017).","journal-title":"Drug Test. Anal."},{"key":"360_CR78","doi-asserted-by":"publisher","first-page":"074002","DOI":"10.1088\/2058-6272\/ab76b4","volume":"22","author":"G WANG","year":"2020","unstructured":"WANG, G. et al. A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy. Plasma Sci. Technol. 22, 074002 (2020).","journal-title":"Plasma Sci. Technol."},{"key":"360_CR79","doi-asserted-by":"publisher","first-page":"8989","DOI":"10.1021\/acsnano.6b05129","volume":"10","author":"Z G\u00f6r\u00f6cs","year":"2016","unstructured":"G\u00f6r\u00f6cs, Z. et al. Quantitative fluorescence sensing through highly autofluorescent, scattering, and absorbing media using mobile microscopy. ACS Nano 10, 8989\u20138999 (2016).","journal-title":"ACS Nano"},{"key":"360_CR80","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12984-017-0255-9","volume":"14","author":"J Howcroft","year":"2017","unstructured":"Howcroft, J., Kofman, J. & Lemaire, E. D. Feature selection for elderly faller classification based on wearable sensors. J. NeuroEng. Rehabil. 14, 47 (2017).","journal-title":"J. NeuroEng. Rehabil."},{"key":"360_CR81","doi-asserted-by":"publisher","first-page":"1650029","DOI":"10.1142\/S0219720016500293","volume":"14","author":"WWB Goh","year":"2016","unstructured":"Goh, W. W. B. & Wong, L. Evaluating feature-selection stability in next-generation proteomics. J. Bioinform. Comput. Biol. 14, 1650029 (2016).","journal-title":"J. Bioinform. Comput. Biol."}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00360-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00360-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00360-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T22:09:52Z","timestamp":1733954992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00360-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,28]]},"references-count":81,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["360"],"URL":"https:\/\/doi.org\/10.1038\/s42256-021-00360-9","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,28]]},"assertion":[{"value":"22 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}