For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs) generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.

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Renqiu buried hill reservoir of electric submersible pump well in order to reduce the sewage invalid recycling and saving energy, using the electric submersible pump(ESP) well convert into pumping unit well to produce. The scheme design lifting process adjustment and optimization of 13 wells. Through the analysis of economic benefit, 13 ESP wells convert into the pumping unit wells will obtain the production economic benefit of 5.5809 million Yuan. The investment recovery period is 24 months. The field application results are obvious, reaching the purpose of energy saving. Keywords-Renqiu buried hill reservoir; electric submersible pump; pumping unit well; lift methods; economic benefit analysis.

An effective method for diagnosing various grades of oral leukoplakia with dysplasia.