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Gene expression data from microarray experiments is widely used for large scale gene expression analysis which facilitates the investigation of fundamental biological processes at molecular level. Such an investigation may be helpful for various biological purposes including disease diagnosis and prognosis, biomarker detection, differentially expressed gene detection, and predicting survival rate...
The recent explosion in availability of gene and protein expression data for cancer detection has necessitated the development of sophisticated machine learning tools for high dimensional data analysis. Previous attempts at gene expression analysis have typically used a linear dimensionality reduction method such as Principal Components Analysis (PCA). Linear dimensionality reduction methods do not...
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), Naïve Bayesian and...
Transcription factors (TFs) and MicroRNAs (miRNAs) are two important regulators of gene expression in cells. Although a wealth of information is available on how TFs bind their targets and carry out their regulatory roles, this is not true for miRNAs. Recent observations suggest a high level of coordination between transcriptional regulation by TFs and post-transcriptional regulation by miRNAs. Understanding...
Self organizing maps (SOMs) portrait molecular phenotypes with individual resolution. We demonstrate the potency of the method in selected applications characterizing the diversity of gene expression in different tissues and cancer subtypes, mRNA and miRNA fingerprints of stem cells, the proteome landscape of algae and genomic relations between humans from different populations. It is further shown...
Many microarray gene expression data sets have multiple ordered sample groups. Genes showing increasing/decreasing differential expression or differential gene-gene co-expression patterns can be biologically interesting. Statistically, we can conduct the analysis of ordered changes of population means and ordered changes of regression slopes. The well-developed isotonic regression can be considered...
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...
A comprehensive understanding of cancer progression may shed light on genetic and molecular mechanisms of oncogenesis, and it may provide much needed information for effective diagnosis, prognosis, and optimal therapy. However, despite considerable effort in studying cancer progressions, their molecular and genetic basis remains largely unknown. Microarray experiments can systematically assay gene...
Genome-wide expression profiles of diseased samples have been exploited to predict disease states. Recently, network-based approaches utilizing molecular interaction networks integrated with gene expression profiles have been proposed to address challenges which arise from smaller number of samples compared to the large number of predictors, and genetic heterogeneity of samples in complex diseases...
In this paper, we present a novel method based on spectral bipartitioning, traditionally used for finding min-cuts in graphs, for classification of cancer using microarray data. Our method is applied to five publicly available datasets of acute leukemia, colon cancer, ovarian cancer, prostate cancer and diffuse large B-cell lymphoma, and is shown to have classification accuracy comparable to that...
Microarray technology has enabled us to simultaneously measure the expression of thousands of genes. Using this high-throughput technology, we can examine subtle genetic changes between biological samples and build predictive models for clinical applications. Although microarrays have dramatically increased the rate of data collection, sample size is still a major issue when selecting features. Previous...
We describe the development of a very large-scale causal, computable model of biology and its specific application in the identification of molecular cause and effect hypotheses of mechanisms underlying the effects of androgen stimulation in the LNCaP prostate carcinoma cell line. In contrast to previous LNCaP studies in which genes have been hierarchically clustered by their pattern of response to...
We describe the development of a very large-scale causal, computable model of biology and its specific application in the identification of molecular cause and effect hypotheses of mechanisms underlying the effects of androgen stimulation in the LNCaP prostate carcinoma cell line. In contrast to previous LNCaP studies in which genes have been hierarchically clustered by their pattern of response to...
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