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Bioinstrumentation and BioMEMS Laboratory

Research Highlight:


Biosensors produce complex outputs which we interpret using machine learning algorithms. We can then visualize grouping of data based on patterns of biomarkers from the original samples.

PUBLICATIONS selected, only algorithm and data analysis work listed here)

Thank you for taking the time to look through our total published work over many years. This page lists the comprehensive total of all of our published work. We publish in several different discipline areas, and we also provide links to sub-categories of our work for easy reference (listed below).

            To view our published work on non-invasive breath analysis, please click HERE.

            To view our published work on agriculture monitoring, please click HERE.

            To view our published work on micro/nano MEMS, instrumentation and novel sensor development, please click HERE.

            To view our published work on algorithms and data analysis, please click HERE.


Peirano DJ, Pasamontes A, Davis CE*. (2016) Supervised Semi-Automated Data Analysis Software for Gas Chromatography / Differential Mobility Spectrometry (GC/DMS) Metabolomics Applications. International Journal for Ion Mobility Spectrometry 19(2): 155-166. DOI: 10.1007/s12127-016-0200-9

Peirano DJ, Aksenov AA, Pasamontes A, Davis CE*. (2013) Chapter 18: Approaches for Establishing Methodologies in Metabolomic Studies for Clinical Diagnostics. Medical Applications of Artificial Intelligence (ed. Arvin Agah). CRC Press, Taylor Francis Group. pp. 279-304. ISBN: 978-1-439-88433-1. LINK

Aksenov AA, Kapron J, Davis CE. (2012) Predicting compensation voltage for singly-charged ions in high-field asymmetric waveform ion mobility spectrometry (FAIMS). Journal of The American Society for Mass Spectrometry 23(10): 1794-1798. DOI: 10.1007/s13361-012-0427-6

Zhao W, Davis CE*. (2011) A modified artificial immune system based pattern recognition approach - An application to clinical diagnostics. Artificial Intelligence in Medicine 52(1): 1-9. DOI: 10.1016/j.artmed.2011.03.001

Schivo M, Akesnov AA, Bardaweel H, Zhao W, Kenyon NJ, Davis CE*. (2011) Building biomarker libraries with novel chemical sensors: correlating differential mobility spectrometer signal outputs with mass spectrometry data. IOP Conference Series: Materials Science and Engineering 18 (2011) 212003 (doi:10.1088/1757-899X/18/21/212003) DOI: 10.1088/1757-899X/18/21/212003

Zhao W, Davis CE*. (2010) Ant colony optimization: a powerful strategy for biomarker feature selection. Swarm Intelligence. (editor L.P. Waters) Nova Science Publishers, Inc. (Hauppauge, NY). ISBN: 978-1-61728-602-5. LINK

Zhao W, Davis CE*. (2009) Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data. Analytica Chimica Acta 651(1): 15-23. (paper reviewed and highlighted in feature article for SeparationNOW) DOI: 10.1016/j.aca.2009.08.008

Zhao W, Sankaran S, Ibanez AM, Dandekar AM, Davis CE*. (2009) Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals. Analytica Chimica Acta 647: 46-53. DOI: 10.1016/j.aca.2009.05.029

Zhao W, Davis CE*. (2009) Autoregressive model based feature extraction method for time shifted chromatography data. Chemometrics and Intelligent Laboratory Systems 96(2): 252-257. DOI: 10.1016/j.chemolab.2009.02.010

Molina M, Zhao W, Sankaran S, Schivo M, Kenyon NJ, Davis CE*. (2008) Design-of-experiment optimization of exhaled breath condensate analysis using a miniature differential mobility spectrometer (DMS). Analytica Chimica Acta 628: 155-161. DOI: 10.1016/j.aca.2008.09.010

Zhao W, Bhushan A, Simon M, Santamaria A, Davis CE*. (2008) Machine learning: a crucial tool for developing sensors. Algorithms 1(2): 130-152. DOI: 10.3390/a1020130

Zhao W, Morgan JT, Davis CE*. (2008) Chemical sensor output classification based on complex coefficients of an autoregressive model. Journal of Sensors 2008: 1-8. DOI: 10.1155/2008/262501

Zhao W, Davis CE*. (2007) Chapter 1: Pattern recognition algorithms to analyze data outputs from novel E-nose sensors. Pattern Recognition in Nanoscience (editor D.M. Perez) Nova Science Publishers, Inc. (Hauppauge, NY), pp. 1-32. ISBN: 978-1-60021-715-9. LINK

Krebs MD, Cohen SJ, Lowzo J, Kang J, Tingley RD, Davis CE*. (2006) Two-dimensional alignment of differential mobility spectrometer data. Sensors and Actuators B - Chemical 119: 475-482. DOI: 10.1016/j.snb.2005.12.058

Krebs MD, Tingley RD, Kang J, Zeskind JE, Holmboe ME, Davis CE*. (2006) Alignment of analytical sensor data by landmark selection from complex chemical mixtures. Chemometrics and Intelligent Laboratory Systems 81(1): 74-81. DOI: 10.1016/j.chemolab.2005.10.001

Shnayderman M, Mansfield B, Yip P, Clark HA, Krebs MD, Cohen SJ, Zeskind JE, Ryan ET, Dorkin HL, Callahan MV, Stair TO, Gelfand JA, Gill CJ, Hitt B, Davis CE*. (2005) Species-specific bacteria identification using differential mobility spectrometry and bioinformatics pattern recognition. Analytical Chemistry 77(18): 5930-5937. DOI: 10.1021/ac050348i

Krebs MD, Tingley RD, Zeskind JE, Kang JM, Holmboe ME, Davis CE*. (2004) Autoregressive modeling of analytical sensor data can yield classifiers in the predictor coefficient parameter space. Bioinformatics 21(8): 1325-31. DOI: 10.1093/bioinformatics/bti160