[1] E. H. Mordan, J. H. Wade, Z. S. B. Wiersma, E. Pearce, T. O. Pangburn, A. W. deGroot, D. M. Meunier, R. C. Bailey. Silicon photonic microring resonator arrays for mass concentration detection of polymers in isocratic separations. Anal. Chem., 2019, 91: 1011-1018 .
[2] R. M. Graybill, C. S. Para, R. C. Bailey. PCR-free, multiplexed expression profiling of microRNAs using silicon photonic microring resonators. Anal. Chem., 2016, 88: 10347-10351 .
[3] J. H. Wade, A. T. Alsop, N. R. Vertin, H. Yang, M. D. Johnson, R. C. Bailey. Rapid, multiplexed phosphoprotein profiling using silicon photonic sensor arrays. ACS Cent. Sci., 2015, 1: 374-382 .
[4] J. H. Wade, R. C. Bailey. Applications of optical microcavity resonators in analytical chemistry. Annu. Rev. Anal. Chem., 2016, 9: 1-25 .
[5] Y. Sun, X. Fan. Optical ring resonators for biochemical and chemical sensing. Anal. Bioanal. Chem., 2011, 399: 205-211 .
[6] C. D. K. Sloan, M. T. Marty, S. G. Sligar, R. C. Bailey. Interfacing lipid bilayer nanodiscs and silicon photonic sensor arrays for multiplexed protein-lipid and protein-membrane protein interaction screening. Anal. Chem., 2013, 85: 2970-2976 .
[7] W. W. Shia, R. C. Bailey. Single domain antibodies for the detection of ricin using silicon photonic microring resonator arrays. Anal. Chem., 2013, 85: 805-810 .
[8] D. Patra, A. Mishra. Recent developments in multi-component synchronous fluorescence scan analysis. TrAC Trends Anal. Chem., 2002, 21: 787-798 .
[9] A. H. Kamal, S. F. El-Malla, S. F. Hammad. A review on UV spectrophotometric methods for simultaneous multicomponent analysis. Eur. J. Pharm. Med. Res., 2016, 3: 348-360 .
[10] S. J. Barton, B. M. Hennelly, T. Ward, K. Domijan, J. Lowry. A review of Raman for multicomponent analysis. Proc. SPIE, 2014, 9129: 91290C .
[11] I. Toumi, S. Caldarelli, B. Torrésani. A review of blind source separation in NMR spectroscopy. Prog. Nucl. Magn. Reson. Spectrosc., 2014, 81: 37-64 .
[12] P. Geladi, B. R. Kowalski. Partial least-squares regression: a tutorial. Anal. Chim. Acta, 1986, 185: 1-17 .
[13] R. Wehrens, B.-H. Mevik. The pls package: principal component and partial least squares regression in R. J. Stat. Softw., 2007, 18: 1-24 .
[14] M. C. U. Araújo, T. C. B. Saldanha, R. K. H. Galvão, T. Yoneyama, H. C. Chame, V. Visani. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics Intellig. Lab. Syst., 2001, 57: 65-73 .
[15] Y. Roggo, P. Chalus, L. Maurer, C. Lema-Martinez, A. Edmond, N. Jent. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharm. Biomed. Anal., 2007, 44: 683-700 .
[16] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 2015, 521: 436-444 .
[17] L. Deng, D. J. F. Yu. Deep learning: methods and applications. Found. Trends Signal Process., 2014, 7: 197-387 .
[18] J. J. N. Schmidhuber. Deep learning in neural networks: an overview. Neural Netw., 2015, 61: 85-117 .
[19] H. M. Robison, P. Escalante, E. Valera, C. L. Erskine, L. Auvil, H. C. Sasieta, C. Bushell, M. Welge, R. C. Bailey. Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification. Integr. Biol., 2019, 11: 16-25 .
[20] J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, B. Li, A. Madabhushi, P. Shah, M. Spitzer, S. Zhao. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 2019, 18: 463-477 .
[21] F. Cheng, Z. Zhao. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc., 2014, 21: e278-e286 .
[22] C. A. Ronao, S.-B. Cho. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl., 2016, 59: 235-244 .
[23] W. Zhao, A. Bhushan, A. D. Santamaria, M. G. Simon, C. E. Davis. Machine learning: a crucial tool for sensor design. Algorithms, 2008, 1: 130-152 .
[24] A. Moraru, M. Pesko, M. Porcius, C. Fortuna, D. J. Mladenic. Using machine learning on sensor data. J. Comput. Inf. Syst., 2010, 18: 341-347 .
[25] M. A. Alsheikh, S. Lin, D. Niyato, H.-P. Tan. Machine learning in wireless sensor networks: algorithms, strategies, and applications. Commun. Surveys Tuts., 2014, 16: 1996-2018 .
[26] Z. Hou, T. Tang, J. Shen, C. Li, F. Li. Prediction network of metamaterial with split ring resonator based on deep learning. Nanoscale Res. Lett., 2020, 15: 83 .
[27] Y. Chen, J. Zhu, Y. Xie, N. Feng, Q. H. Liu. Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network. Nanoscale, 2019, 11: 9749-9755 .
[28] W. Ma, F. Cheng, Y. Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano, 2018, 12: 6326-6334 .
[29] J. S. T. Smalley, Y. Zhao, A. A. Nawaz, Q. Hao, Y. Ma, I.-C. Khoo, T. J. Huang. High contrast modulation of plasmonic signals using nanoscale dual-frequency liquid crystals. Opt. Express, 2011, 19: 15265-15274 .
[30] M. Ian Lapsley, A. Shahravan, Q. Hao, B. Krishna Juluri, S. Giardinelli, M. Lu, Y. Zhao, I.-K. Chiang, T. Matsoukas, T. J. Huang. Shifts in plasmon resonance due to charging of a nanodisk array in argon plasma. Appl. Phys. Lett., 2012, 100: 101903 .
[31] R. A. Potyrailo, J. E. Brewer, B. Cheng, M. Carpenter, N. M. Houlihan, A. Kolmakov. Bio-inspired gas sensing: boosting performance with sensor optimization guided by ‘machine learning’. Faraday Discuss., 2020, 223: 161-182 .
[32] Z. S. Ballard, D. Shir, A. Bhardwaj, S. Bazargan, S. Sathianathan, A. Ozcan. Computational sensing using low-cost and mobile plasmonic readers designed by machine learning. ACS Nano, 2017, 11: 2266-2274 .
[33] X. Feng, G. Zhang, L. K. Chin, A. Q. Liu, B. Liedberg. Highly sensitive, label-free detection of 2,4-dichlorophenoxyacetic acid using an optofluidic chip. ACS Sens., 2017, 2: 955-960 .
[34] Y. Shi, H. Zhao, K. T. Nguyen, Y. Zhang, L. K. Chin, T. Zhu, Y. Yu, H. Cai, P. H. Yap, P. Y. Liu, S. Xiong, J. Zhang, C.-W. Qiu, C. T. Chan, A. Q. Liu. Nanophotonic array-induced dynamic behavior for label-free shape-selective bacteria sieving. ACS Nano, 2019, 13: 12070-12080 .
[35] Y. Shi, H. Zhao, L. K. Chin, Y. Zhang, P. H. Yap, W. Ser, C.-W. Qiu, A. Q. Liu. Optical potential-well array for high-selectivity, massive trapping and sorting at nanoscale. Nano Lett., 2020, 20: 5193-5200 .
[36] Z. Li, J. Zou, H. Zhu, B. T. T. Nguyen, Y. Shi, P. Y. Liu, R. C. Bailey, J. Zhou, H. Wang, Z. Yang, Y. Jin, P. H. Yap, H. Cai, Y. Hao, A. Q. Liu. Biotoxoid photonic sensors with temperature insensitivity using a cascade of ring resonator and Mach-Zehnder interferometer. ACS Sens., 2020, 5: 2448-2456 .
[37] Abadi M. Barham P. Chen J. Chen Z. Davis A. Dean J. Devin M. Ghemawat S. Irving G. Isard M. , “TensorFlow: a system for large-scale machine learning ,” in 12th USENIX Symposium on Operating Systems Design and Implementation (2016 ), pp. 265 –283 .
[38] Zhang H. Karim M. F. Zheng S. Cai H. Gu Y. Chen S. S. Yu H. Liu A. Q. , “A high-resolution dual-microring-based silicon photonic sensor using electronic integrated circuit ,” in CLEO: Applications and Technology (Optical Society of America , 2018 ), paper ATh4O.4 .
[39] Zhang H. Karim M. F. Zheng S. Cai H. Gu Y. Chen S. S. Yu H. Liu A. Q. , “Machine learning and silicon photonic sensor for complex chemical components determination ,” in CLEO: Science and Innovations (Optical Society of America , 2018 ), paper JW2A.54 .
[40] M. C. Cardenosa-Rubio, H. M. Robison, R. C. Bailey. Recent advances in environmental and clinical analysis using microring resonator-based sensors. Curr. Opin. Environ. Sci. Health, 2019, 10: 38-46 .