Backgroud:
The impact of cancer in the society has created the necessity of new and faster theoretical models for the early diagnosis of cancer.
Methods:
In the work, A mass spectrometry (MS) data analysis method based on 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. And then, the topological indexes of the star-like graph are calculated to describe each MS data of 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 the model were 94.43%, 96.25%, and 91.11%, respectively, for [-1,1] normalization data.
Conclusion:
The model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.
CLMSVault: A Software Suite for Protein Cross-Linking Mass-Spectrometry Data Analysis and Visualization