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Analyzing the effects of various environmental factors on human diseases is one of the important issues in recent bioinformatics studies. In this paper we investigate several environmental factors regarding Type-2 diabetes and select some of them for develop an analytical model of disease risk prediction. For the selection of significant factors, we first preprocessed all the environmental factors...
SNP association study has been widely performed to find disease-related genetic markers usually by investigating the difference of SNP genotype frequencies between disease and non-disease samples and evaluating its significance in a statistical sense. However, such approach often incurs the problem of producing tie scores over multiple SNPs, especially when the number of samples is not large enough...
For the identification of significant genes involved in specific diseases, microarray gene expression profiles have been widely used to prioritize candidate genes. In this paper, we propose a new gene ranking method that employs genegene relations extracted from literature along with gene expression scores obtained from microarrays. Here the genegene relations are extracted by taking a hybrid approach...
Recently multimodal biometrics technology that employs more than two types of biometrics data has been popularly used for person authentication and verification. In particular, the score-level fusion approach which combines matching scores from unimodal systems to make final decision has gained lots of attentions. In most of these works, however, they assume all the matching scores to be of the same...
Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene-sets showing differential expression between two groups. In particular, unlike other previous approaches, this enables us to uncover their biological meanings in an elegant way by providing a unified analytical framework that employs a priori known biological knowledges along with gene expression...
Modeling to predict fault-proneness of software modules is an important area of research in software engineering. Most such models employ a large number of basic and derived metrics as predictors. This paper presents modeling results based on only two metrics, lines of code and cyclomatic complexity, using radial basis functions with Gaussian kernels as classifiers. Results from two NASA systems are...
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