Assessing the Statistical Significance of the Achieved Classification Error of Classifiers Constructed using Serum Peptide Profiles, and a Prescription for Random Sampling Repeated Studies for Massive High-Throughput Genomic and Proteomic Studies
| Autor: | William L Bigbee, Milos Hauskrecht, Herbert J Zeh III, David C Whitcomb, David E Malehorn, James Lyons-Weiler, Richard Pelikan |
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| Jazyk: | angličtina |
| Rok vydání: | 2005 |
| Předmět: |
Cancer Research
030503 health policy & services pancreatic cancer biomarkers bioinformatics prostate cancer lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens lcsh:RC254-282 03 medical and health sciences ovarian cancer proteomics 0302 clinical medicine ComputingMethodologies_PATTERNRECOGNITION Oncology disease prediction models 030212 general & internal medicine early detection 0305 other medical science |
| Zdroj: | Cancer Informatics, Vol 1 (2005) Cancer Informatics, Vol 1, Pp 53-77 (2005) |
| ISSN: | 1176-9351 |
| Popis: | Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance. In this work we describe a computational validation framework, called PACE (Permutation-Achieved Classification Error), that lets us assess, for a given classification model, the significance of the Achieved Classification Error (ACE) on the profile data. The framework compares the performance statistic of the classifier on true data samples and checks if these are consistent with the behavior of the classifier on the same data with randomly reassigned class labels. A statistically significant ACE increases our belief that a discriminative signal was found in the data. The advantage of PACE analysis is that it can be easily combined with any classification model and is relatively easy to interpret. PACE analysis does not protect researchers against confounding in the experimental design, or other sources of systematic or random error. We use PACE analysis to assess significance of classification results we have achieved on a number of published data sets. The results show that many of these datasets indeed possess a signal that leads to a statistically significant ACE. |
| Databáze: | OpenAIRE |
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