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    Procedure for developing linear and Bayesian classification models based on immunosensor measurements

    Year: 2014

    Journal: Sensors and Actuators B: Chemical Volume 190, January 2014, Pages 165–170, 20131003

    Authors: Stephen Mobley 1, Sunil Yalamanchili 1, Hongzheng Zhang 2, Rossella Marullo 2, Zhuo G. Chen 2, Carlos S. Moreno 3, Dong M. Shin 2, Paul W. Doetsch 2, William D. Hunt 1

    Last authors: William D. Hunt

    Organizations: 1 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA 2 Department of Hematology and Medical Oncology, Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA 3 Department of Pathology & Laboratory Medicine, Whitehead Research Building of Emory University, Atlanta, GA 30322, USA

    Country: USA, US, United States, United States of America, America

    A protocol for the creation of a set of classification models was developed to differentiate between biological samples based on immunosensor measurements. For this paper, data was gathered using Au Quartz Crystal Microbalance with Dissipation (QCM-D) sensors inoculated with an alkanethiol self-assembling monolayer functionalized for the detection of pAkt, γH2AX, β-Actin, and FITC antigen expression. Oropharyngeal cancer lysate samples, both positive (SCC47) and negative (TU212) for high risk human papillomavirus (HPV16), were used to gather the classification model training data set. Subsequently, linear and Bayesian classifiers were formulated based on the feature values and defined linear discriminant functions. The following study distinguishes between HPV-positive and HPV-negative cell lines, yet these guidelines can be utilized for different immunosensor platforms and disease diagnosis.