Abstract:

Mass spectrometry is commonly used for disease prevention and diagnosis， but the large number of mass spectrometry data features and the wide variation of features among different diseases make the task of multi-disease diagnosis complex and difficult. To solve the above problems， this paper proposes the generative adversarial autoencoder integrated voting algorithm msDAGVote based on mass spectrometry data. The msDAGVote feature extraction framework uses a dual autoencoder-based generative adversarial network， and after the network has been trained by mass spectrometry data， the generator sub-network is used for feature construction. Evaluated using mass spectrometry datasets of 10 different disease types， the experimental data show that msDAGVote extracts better features than comparative method， significantly reduces the number of features required for classification while providing excellent diagnostic power for disease classification， with classification AUC over 0.98 on six datasets and 0.87 on the remaining challenging datasets.

Key words: computer application, bioinformatics, mass spectrometry, feature engineering, feature selection, generative adversarial network, dual autoencoder