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Schizophrenia (SZ) is a complex disease caused by a lot genetic variants, epigenetic and brain region abnormalities. In this study, we adopted a joint nonnegative matrix factorization method to integrate three datasets including single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA Methylation to identify multi-dimensional modules associated...
Single Nucleotide Polymorphisms (SNPs) are the most common form of genetic variation in humans comprising nearly 1/1,000th of the average human genome. The intelligent analysis of databases may be affected by the presence of unimportant features, which motivates the application of feature selection. In this work, we have proposed a genetic based feature selection. Genetic algorithm (GA) is a search...
Medical diagnosis is a major area of current research in Machine Learning and Data Mining. Single Nucleotide Polymorphisms (SNPs) are an important source of the human genome variability and have thus been implicated in several human diseases, including cancer. Breast Cancer is the most common malignant tumour for women and has known a large spread during the past twenty years. To separate the tumorous...
We present the application of a regularized least-squares based algorithm, known as greedy RLS, to perform a wrapper-based feature selection on an entire genome-wide association dataset. Wrapper methods were previously thought to be computationally infeasible on these types of studies. The running time of the method grows linearly in the number of training examples, the number of features in the original...
This paper describes research on the use of feature selection techniques to find correlation between single-nucleotide-polymorphism (SNP) in genes with the lupus disease in Genome-Wide Association (GWA) study. Feature selection is the process of selecting features that are correlated and discarding features that have no correlation in data mining. In this research, feature selection techniques will...
In the recent years, Genome-Wide Association Study (GWAS) has been performed by many scientist around the world to find association between genetic profiles of different individuals with the risk of developing certain diseases. GWAS are performed using the Single Nucleotide Polymorphism (SNP) data which represents the genotypes of two different groups of individuals: the case group of individuals...
With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. One main challenge for this study is to find the optimal subsets of Single Nucleotide Polymorphisms (SNPs) which are most tightly associated with diseases. Feature selection which might...
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