AI Article Synopsis
- Fluorescence spectroscopy is recognized as a promising technique for detecting cervical cancer.
- Researchers identified the importance of robust smoothing in the data preprocessing stage to effectively differentiate the signal from background noise.
- The study critiques existing methods in R for smoothing parameter selection and offers a new, computationally efficient approach that approximates leave-one-out cross validation.
Article Abstract
Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2923818 | PMC |
| http://dx.doi.org/10.1016/j.csda.2009.08.001 | DOI Listing |
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