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Mass spectrometry (MS) data has been widely analyzed for the detection of early stage cancers. Its potential for seeking proteomic biomarkers has received a great deal of attention in recent years. In the sparse representation classification (SRC) framework, a testing sample is represented as a sparse linear combination of training samples. The coefficient vector of representation is obtained by a...
Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although numerous methods were proposed and achieved promising results in structural class prediction, some problems in using protein-sequence information have impeded the development. In this paper, a combined representation of protein-sequence information is proposed for prediction of protein structural class,...
Ovarian Carcinoma (OvCa) is the most lethal type of gynecological cancer. The studies show that about 90% patients could be saved if they are treated in the early stage. In this study, a novel biomarker selection approach is proposed which combines singular value decomposition (SVD) and Monte Carlo strategy to early OvCa detection. Other than supervised classification methods or differential expression...
Many studies have been proposed to identify gene makers that are associated with cancers, but the found markers are approach dependent. For example, the results are correlated with classifiers in supervised feature selection, and many of them didn't consider the influences of other factors, such as the grades or stages of cancers. In this study, we proposed a supervised SVD approach to extract the...
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