Lifeng Peng

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The sequence patterns of 11 types of frequently occurring connecting peptides, which lead to a classification of supersecondary motifs, were studied. A database of protein supersecondary motifs was set up. An artificial neural network method, i.e. the back propagation neural network, was applied to the predictions of the supersecondary motifs from protein(More)
—Biomarker detection in LC-MS data depends mainly on the feature selection algorithm as the number of features is extremely high while the number of samples is very small. This makes the classification of these data sets extremely challenging. In this paper we propose the use of genetic programming (GP) for subset feature selection in LC-MS data which works(More)
Glioblastoma multiforme (GBM), an aggressive tumor that typically exhibits treatment failure with high mortality rates, is associated with the presence of cancer stem cells (CSCs) within the tumor. CSCs possess the ability for perpetual self-renewal and proliferation, producing downstream progenitor cells that drive tumor growth. Studies of many cancer(More)
The use of mass spectrometry to verify and quantify biomarkers requires the identification of the peptides that can be detectable. In this paper, we propose the use of genetic programming (GP) to measure the detection probability of the peptides. The new GP method is tested and verified on two different yeast data sets with increasing complexity and shows(More)
Biomarker identification, i.e., detecting the features that indicate differences between two or more classes, is an important task in omics sciences. Mass spectrometry (MS) provide a high throughput analysis of proteomic and metabolomic data. The number of features of the MS data sets far exceeds the number of samples, making biomarker identification(More)