Abstract
Background: The impact of cancer in society created the necessity of new and faster theoretical models for the early diagnosis of cancer.
Methods: In this work, a mass spectrometry (MS) data analysis method based on the star-like graph of protein and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the MS data set. Firstly, the MS data is reduced and transformed into the corresponding protein sequence. Then, the topological indexes of the star-like graph are calculated to describe each MS data of the cancer sample. Finally, the SVM model is suggested to classify the MS data.
Results: Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the model were 96.45%, 96.88%, and 95.67%, respectively, for [0,1] normalization data, and 94.43%, 96.25%, and 91.11% for [-1,1] normalization data.
Conclusion: The model combined with the SELDI-TOF-MS technology has a prospect in early clinical detection and diagnosis of ovarian cancer.
Keywords: Ovarian cancer, mass spectrometry, topological indices, star-like graph, support vector machine, proteomics.
Graphical Abstract
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