Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed t...Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.展开更多
Objective To explore the expression of macrophage capping protein(CapG)in colorectal carcinoma tissues,and to investigate its effects on proliferation and migration of colorectal carcinoma cells.Methods From September...Objective To explore the expression of macrophage capping protein(CapG)in colorectal carcinoma tissues,and to investigate its effects on proliferation and migration of colorectal carcinoma cells.Methods From September10th,2015 to March 2nd,2016,the clinical data and tissues specimen of 84 patients with colorectal展开更多
Proteomics become an important research area of interests in life science after the completion of the human genome project.This scientific is to study the characteristics of proteins at the large-scale data level,and ...Proteomics become an important research area of interests in life science after the completion of the human genome project.This scientific is to study the characteristics of proteins at the large-scale data level,and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level.A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies.Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era,such as protein-protein interactions(PPIs).In this review,we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects.First,we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources.Second,we describe the state-of-the-art computational methods recently proposed on this topic.Finally,we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61232001,61502166,61502214,61379108,and 61370024)Scientific Research Fund of Hunan Provincial Education Department(Nos.15CY007 and 10A076)
文摘Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.
文摘Objective To explore the expression of macrophage capping protein(CapG)in colorectal carcinoma tissues,and to investigate its effects on proliferation and migration of colorectal carcinoma cells.Methods From September10th,2015 to March 2nd,2016,the clinical data and tissues specimen of 84 patients with colorectal
基金This work was supported in part by Awardee of the NSFC Excellent Young Scholars Program in 2017,in part by the National Natural Science Foundation of China(Grant Nos.61902342,61722212 and 61572506).
文摘Proteomics become an important research area of interests in life science after the completion of the human genome project.This scientific is to study the characteristics of proteins at the large-scale data level,and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level.A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies.Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era,such as protein-protein interactions(PPIs).In this review,we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects.First,we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources.Second,we describe the state-of-the-art computational methods recently proposed on this topic.Finally,we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.
