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Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW),...
A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect...
In this paper, we proposed a new data fusion method to infer gene regulatory networks based on differential equations model. After testing on several simulation and real data sets, and comparing with three other kinds of single fusion methods, the results show that our method is effective and better than other 3 fusion methods in inferring networks.
With the development of microarray technology, a lot of gene expression datasets have been applied to cancer classification and biomarker detection. Most of these gene expression datasets have small number of samples and tens of thousands of genes, so irrelevant genes eliminating is an important stage of feature selection for microarray expression data analysis. In this paper, an improved global normalized...
Gene expression data possess two main features: small samples and high dimensions. There are many difficulties on analyzing gene expression data using the traditional machine learning methods. In this paper we use an SVM-RFE based method to obtain the set of trait genes that are related to the disease-resistance property in rice and evaluate these genes according to some heuristics. And then we query...
Cluster validation techniques are essential tools within cluster analysis, helpful to the interpretation of clustering results. In this study, the validation ability of Dunn's index in gene clustering was investigated with public gene expression datasets clustered by hierarchical clustering, K-means and Self-organizing maps. It was made clear that Dunn's index would give misleading validity results...
Traditional GEP algorithm takes up many system resources in decoding and evaluating due to the operation of the tree construction and corresponding traversing. This paper aims to introduce a novel GEP algorithm to alleviate the drawback mentioned above. The main contributions include:(1) presenting a new method for decoding and evaluating chromosome (SGDE), and proposing the corresponding ETs construction...
In this paper, we apply the least-square support vector machine (LS-SVM) to operon prediction of Escherichia coli (E.coli), with different combinations of intergenic distance, gene expression data, and phylogenetic profile. Experimental results demonstrate that the WO pairs tend to have shorter intergenic distances, higher correlation coefficient and much stronger relation of co-envoled between phylogenetic...
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