A neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer

dc.contributor.authorZhang, Fan
dc.contributor.authorChen, Jake
dc.contributor.authorWang, Mu
dc.contributor.authorDrabier, Renee
dc.contributor.departmentComputer and Information Science, Purdue School of Science
dc.date.accessioned2025-04-16T08:20:09Z
dc.date.available2025-04-16T08:20:09Z
dc.date.issued2013
dc.description.abstractBackground: In the past several years, there has been increasing interest and enthusiasm in molecular biomarkers as tools for early detection of cancer. Liquid chromatography tandem mass spectrometry (LC/MS/MS) based plasma proteomics profiling technique is a promising technology platform to study candidate protein biomarkers for early detection of cancer. Factors such as inherent variability, protein detectability limitation, and peptide discovery biases among LC/MS/MS platforms have made the classification and prediction of proteomics profiles challenging. Developing proteomics data analysis methods to identify multi-protein biomarker panels for breast cancer diagnosis based on neural networks provides hope for improving both the sensitivity and the specificity of candidate cancer biomarkers for early detection. Results: In our previous method, we developed a Feed Forward Neural Network-based method to build the classifier for plasma samples of breast cancer and then applied the classifier to predict blind dataset of breast cancer. However, the optimal combination C* in our previous method was actually determined by applying the trained FFNN on the testing set with the combination. Therefore, in this paper, we applied a three way data split to the Feed Forward Neural Network for training, validation and testing based. We found that the prediction performance of the FFNN model based on the three way data split outperforms our previous method and the prediction performance is improved from (AUC = 0.8706, precision = 82.5%, accuracy = 82.5%, sensitivity = 82.5%, specificity = 82.5% for the testing set) to (AUC = 0.895, precision = 86.84%, accuracy = 85%, sensitivity = 82.5%, specificity = 87.5% for the testing set). Conclusions: Further pathway analysis showed that the top three five-marker panels are associated with complement and coagulation cascades, signaling, activation, and hemostasis, which are consistent with previous findings. We believe the new approach is a better solution for multi-biomarker panel discovery and it can be applied to other clinical proteomics.
dc.eprint.versionFinal published version
dc.identifier.citationZhang F, Chen J, Wang M, Drabier R. A neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer. BMC Proc. 2013;7(Suppl 7):S10. doi:10.1186/1753-6561-7-S7-S10
dc.identifier.urihttps://hdl.handle.net/1805/47059
dc.language.isoen_US
dc.publisherSpringer Nature
dc.relation.isversionof10.1186/1753-6561-7-S7-S10
dc.relation.journalBMC Proceedings
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourcePMC
dc.subjectMolecular biomarkers
dc.subjectEarly cancer detection
dc.subjectLiquid chromatography tandem mass spectrometry (LC/MS/MS)
dc.subjectProteomics data analysis
dc.titleA neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer
dc.typeArticle
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