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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,...
Gene expression data analysis is a very useful tool for medical diagnosis. Combined with classification methods, this technology can be used to help make clinical decisions for individual patients. In this paper, a novel classification method for cancer microarray data was proposed. This method includes two stages: The first stage is to select a number of genes based on a gene selection algorithm,...
According to the characteristics of transliterated names in Chinese texts, a method of automatic recognition of Chinese transliterated names combining support vector machines (SVMs) with rules is proposed. The attributes of feature vectors based on characters are extracted. A training set is established and the machine learning models of automatic identification of transliterated names are obtained...
Ovarian cancer is the fifth leading cause of cancer death among women in the United States and western Europe. Platinum drugs are the most active agents in epithelial ovarian cancer therapy. In order to improve the prediction of response to platinum-based chemotherapy for advanced-stage ovarian cancers, we describe an integrated model which combines clinical information tumor and treatment information,...
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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