AI Article Synopsis
- The study aimed to differentiate between 24 disease spectra and 17 normal spectra using data processing techniques on proteomics data from a conference.
- Key processing steps included correcting sinusoidal noise, normalizing data, and identifying polymeric peaks that aided in distinguishing normal spectra.
- The analysis yielded a high classification accuracy of 92.6% by focusing on specific mass to charge (m/z) values, and could be improved further by incorporating two additional peak values identified through a genetic algorithm.
Article Abstract
For our analysis of the data from the First Annual Proteomics Data Mining Conference, we attempted to discriminate between 24 disease spectra (group A) and 17 normal spectra (group B). First, we processed the raw spectra by (i) correcting for additive sinusoidal noise (periodic on the time scale) affecting most spectra, (ii) correcting for the overall baseline level, (iii) normalizing, (iv) recombining fractions, and (v) using variable-width windows for data reduction. Also, we identified a set of polymeric peaks (at multiples of 180.6 Da) that is present in several normal spectra (B1-B8). After data processing, we found the intensities at the following mass to charge (m/z) values to be useful discriminators: 3077, 12 886 and 74 263. Using these values, we were able to achieve an overall classification accuracy of 38/41 (92.6%). Perfect classification could be achieved by adding two additional peaks, at 2476 and 6955. We identified these values by applying a genetic algorithm to a filtered list of m/z values using Mahalanobis distance between the group means as a fitness function.
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| http://dx.doi.org/10.1002/pmic.200300522 | DOI Listing |
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