- Meeting abstract
- Open Access
Prediction of peptide drift time in ion mobility-mass spectrometry
© Wang et al; licensee BioMed Central Ltd. 2009
- Published: 25 June 2009
- Artificial Neural Network
- Hide Layer
- Charge State
- Protein Identification
Experimental datasets with different charge state assignment
Charge state assignment
Number of peptides
In this study, an ANN regression model was developed to predict peptide drift time in IMMS. Three peptide datasets with different peptide charge states were used to train the predictor to capture the differences of drift time among the varied peptides. The high performance of predictor indicated the capacity of our proposed method. In addition, a simple net architecture, which consisted of an input layer with four neurons, a hidden layer with four nodes and an output layer with one neuron, make our model more effective for application of protein identification.
This project was funded by 1R41RR024306.
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This article is published under license to BioMed Central Ltd.