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Human saliva is rich in proteins, which have been used for disease detection such as oral diseases and systematic diseases. In this paper, we present a computational method for predicting secretory proteins in human saliva based on two sets of human proteins from published literatures and public databases. One set contains known proteins which can be secreted into saliva, and the other contains the...
In this paper, we present a practical algorithm to deal with the data specific classification problem when there are datasets with different properties. We proposed to integrate error rate, missing values and expert judgment as factors for determining data specific pruning to form Expert Knowledge Based Pruning (EKBP). We conduct an extensive experimental study on openly available 40 real world datasets...
The identification of effective biomarkers for preventive intervention or targeted therapies will increase survival rate of cancer patients dramatically. However, the unclear molecular mechanism of carcinogenesis still blocks the discovery process of effective cancer biomarkers. Network-based analyses have been introduced into computational biomarker discovery for many years. The random walks ranking...
In this paper, we investigated the use of gene coexpression network analyses to identify potential biomarkers for breast carcinoma prognosis. The network mining algorithm CODENSE is used to identify highly connected genome-wide gene co-expression networks among a variety of cancer types, and the resulted gene clusters are applied to a series of breast cancer microarray sets to categorize the patients...
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