Procedure for developing linear and Bayesian classification models based on immunosensor measurements
| Autor: | William D. Hunt, Stephen Mobley, Sunil Yalamanchili, Dong M. Shin, Rossella Marullo, Zhuo Georgia Chen, Hongzheng Zhang, Carlos S. Moreno, Paul W. Doetsch |
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| Rok vydání: | 2014 |
| Předmět: |
Training set
Computer science business.industry Bayesian probability Metals and Alloys Nanotechnology Pattern recognition Quartz crystal microbalance Condensed Matter Physics Linear discriminant analysis Surfaces Coatings and Films Electronic Optical and Magnetic Materials Set (abstract data type) Naive Bayes classifier Pattern recognition (psychology) Materials Chemistry Feature (machine learning) Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | Sensors and Actuators B: Chemical. 190:165-170 |
| ISSN: | 0925-4005 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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