microarray expression datasets generate a huge number of genes, but only a few genes provide information about cancer diseases. In this context, feature selection approaches have been developed to deal with this probl...
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microarray expression datasets generate a huge number of genes, but only a few genes provide information about cancer diseases. In this context, feature selection approaches have been developed to deal with this problem. Filter-based methods, in particular, select the relevant genes and remove the irrelevant ones using different evaluation metrics. In this study, we shed light on nine univariate filter methods. Three categories of filter methods were investigated using eight microarray datasets, including binary and multi-class samples. The support vector machine and Naive Bayes classifiers were used to assess classification accuracy. Different comparison methods were used to assist the researchers in visualizing the performance of each studied filter. Precisely, statistical tests were applied in terms of classification accuracy, and the feature ranking similarity of the filter methods was studied based on a rank correlation measure.
Background: It is well established that caspase-1 exerts its biological activities through its downstream targets such as IL-1 beta, IL-18, and Sirt-1. The microarray datasets derived from various caspase-1 knockout t...
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Background: It is well established that caspase-1 exerts its biological activities through its downstream targets such as IL-1 beta, IL-18, and Sirt-1. The microarray datasets derived from various caspase-1 knockout tissues indicated that caspase-1 can significantly impact the transcriptome. However, it is not known whether all the effects exerted by caspase-1 on transcriptome are mediated only by its well-known substrates. Therefore, we hypothesized that the effects of caspase-1 on transcriptome may be partially independent from IL-1 beta, IL-18, and Sirt-1. Methods: To determine new global and tissue-specific gene regulatory effects of caspase-1, we took novel microarray data analysis approaches including Venn analysis, cooperation analysis, and meta-analysis methods. We used these statistical methods to integrate different microarray datasets conducted on different caspase-1 knockout tissues and datasets where caspase-1 downstream targets were manipulated. Results: We made the following important findings: (1) Caspase-1 exerts its regulatory effects on the majority of genes in a tissue-specific manner;(2) Caspase-1 regulatory genes partially cooperates with genes regulated by sirtuin-1 during organ injury and inflammation in adipose tissue but not in the liver;(3) Caspase-1 cooperates with IL-1 beta in regulating less than half of the genes involved in cardiovascular disease, organismal injury, and cancer in mouse liver;(4) The meta-analysis identifies 40 caspase-1 globally regulated genes across tissues, suggesting that caspase-1 globally regulates many novel pathways;and (5) The meta-analysis identified new cooperatively and non-cooperatively regulated genes in caspase-1, IL-1 beta, IL-18, and Sirt-1 pathways. Conclusions: Our findings suggest that caspase-1 regulates many new signaling pathways potentially via its known substrates and also via transcription factors and other proteins that are yet to be identified.
Type 2 diabetes mellitus (T2DM) is a non-communicable, metabolic disorder in which the loss of function and/or mass of pancreatic 8-cells occurs. The incidence of T2DM is increasing worldwide and needs more efficient ...
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Type 2 diabetes mellitus (T2DM) is a non-communicable, metabolic disorder in which the loss of function and/or mass of pancreatic 8-cells occurs. The incidence of T2DM is increasing worldwide and needs more efficient biomarkers for diagnosis along with better therapeutic interventions with fewer side effects. Transcriptomics analysis provides a complete overview of the molecular mechanisms involved at the system level to identify the set of genes that have stayed unnoticed so far for complex diseases, like T2DM. This study has been conducted to identify novel hub gene candidates for T2DM. Five human GEO datasets from pancreatic islets of normal healthy volunteers and T2DM subjects, namely, GSE118139, GSE20966, GSE25724, GSE38642 and GSE41762 were chosen and the differentially expressed genes were shortlisted. The overlapping hub genes between these datasets were identified, which included ERBB2, HGF, MET, MMP1, PDGFRA and PLAU as downregulated genes;and ABCC8, G6PC2, IAPP, PCSK1 and SLC2A2 as up-regulated genes in the pancreatic islets from T2DM subjects. These hub genes could be developed as a panel for diagnosis/prognosis of T2DM in the population and can be targeted for drug development for more effective management of T2DM in the future.
The main goal of successful microarray data classification is to reduce the computational time while improving the classification accuracy. Though a large pool of techniques are already available, accurate classificat...
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ISBN:
(纸本)9781479974566
The main goal of successful microarray data classification is to reduce the computational time while improving the classification accuracy. Though a large pool of techniques are already available, accurate classification of normal and malignant tissue cells is very challenging for the diagnosis of various types of cancers in humans. In this paper, Support Vector Machines (SVM), Naive Bayesian and k-Nearest neighbor classifiers are used for classification of publicly available biomedical microarray datasets such as Prostate cancer, Leukemia and Colon tumor. To overcome the curse of dimensionality, Modified Particle Swarm Optimization (MPSO) is used to select the features from the datasets. A number of useful performance evaluation measures including classification accuracy, precision, recall, F-score as well as the area under the receiver operating characteristic curve are taken to evaluate the classifiers. After analyzing the experimental results, it is verified that SVM outperforms other classifiers and the performance is even improved a lot after feature selection.
Type 2 diabetes mellitus (T2DM) is a non-communicable, metabolic disorder in which the loss of function and/or mass of pancreatic β-cells occurs. The incidence of T2DM is increasing worldwide and needs more efficient...
详细信息
Type 2 diabetes mellitus (T2DM) is a non-communicable, metabolic disorder in which the loss of function and/or mass of pancreatic β-cells occurs. The incidence of T2DM is increasing worldwide and needs more efficient biomarkers for diagnosis along with better therapeutic interventions with fewer side effects. Transcriptomics analysis provides a complete overview of the molecular mechanisms involved at the system level to identify the set of genes that have stayed unnoticed so far for complex diseases, like T2DM. This study has been conducted to identify novel hub gene candidates for T2DM. Five human GEO datasets from pancreatic islets of normal healthy volunteers and T2DM subjects, namely, GSE118139, GSE20966, GSE25724, GSE38642 and GSE41762 were chosen and the differentially expressed genes were shortlisted. The overlapping hub genes between these datasets were identified, which included ERBB2, HGF, MET, MMP1, PDGFRA and PLAU as upregulated genes; and ABCC8, G6PC2, IAPP, PCSK1 and SLC2A2 as downregulated genes in the pancreatic islets from T2DM subjects. These hub genes could be developed as a panel for diagnosis/prognosis of T2DM in the population and can be targeted for drug development for more effective management of T2DM in the future.
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