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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...
Protein mass spectrometry has become a popular tool for cancer diagnosis. Feature selection and classification techniques play an important role in the identification of protein biomarkers. In this paper, based on the protein spectrum of cancer classification, an efficient combination of wavelet features and Recursive Null Space LDA algorithm for feature selection is proposed. Firstly, the multi-resolution...
Ovarian cancer (OvCa) has become one of the most lethal gynecological cancers in the world. The identification of ovarian cancer linked biomarkers will provide the basis of diagnoses and treatment. In this study, we proposed to combine singular value decomposition (SVD) and Monte Carlo method to analyze the OvCa data and predict the outcomes of samples. A supervised SVD was proposed to weight biomarkers...
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...
Breast cancer has become one of the most dangerous tumors for middle-aged and older women in China recently. Mammography is its most reliable detection method in the clinic, and computer-aided diagnosis (CAD) could assist the radiologists in reading the mammograms. In this paper, a new algorithm was proposed to estimate the skin-line of the breast automatically, which could be divided into four steps...
It is a challenge to construct a reliable classifier based on microarray gene expression data for prediction of chemotherapy response, because usually only a small number of samples are available and each sample has thousands of gene expressions. This paper uses boosting and bootstrap approaches to improve the reliability of prediction. Specifically, AdaBoost and multiple classifiers based methods...
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