• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.248-255
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• 2005

To understand the response of cancer cells to anticancer drugs at the gene expression level, we examined the gene expression changes in response to five anticancer drugs, 5-fluorouracil, cytarabine, cisplatin, paclitaxel, and cytochalasin D in NCI-H460 human lung cancer cells. Of the five drugs, 5-fluorouracil had the most distinctive gene expression signature. By clustering genes whose expression changed significantly, we identified three clusters with unique gene expression patterns. The first cluster reflected the up-regulation of gene expression by cisplatin, and included genes involved in cell death and DNA repair. The second cluster pointed to a general reduction of gene expression by most of the anticancer drugs tested. A number of genes in this cluster are involved in signal transduction that is important for communication between cells and reception of extracellular signals. The last cluster represented reduced gene expression in response to 5-fluorouracil, the genes involved being implicated in DNA metabolism, the cell cycle, and RNA processing. Since the gene expression signature of 5-fluorouracil was unique, we investigated it in more detail. Significance analysis of microarray data (SAM) identified 808 genes whose expression was significantly altered by 5-fluorouracil. Among the up-regulated genes, those affecting apoptosis were the most noteworthy. The down-regulated genes were mainly associated with transcription-and translation-related processes which are known targets of 5-fluorouracil. These results suggest that the gene expression signature of an anticancer drug is closely related to its physiological action and the response of caner cells.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• Journal of Information Processing Systems
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• v.3 no.1
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• pp.38-42
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• 2007

Microarray data includes tens of thousands of gene expressions simultaneously, so it can be effectively used in identifying the phenotypes of diseases. However, the retrieval of functional information from a large corpus of gene expression data is still a time-consuming task. In this paper, we propose an efficient method for identifying functional categories of differentially expressed genes from a micro-array experiment by using Gene Ontology (GO). Our method is as follows: (1) The expression data set is first filtered to include only genes with mean expression values that differ by at least 3-fold between the two groups. (2) The genes are then ranked based on the t-statistics. The 100 most highly ranked genes are selected as informative genes. (3) The t-value of each informative gene is imposed as a score on the associated GO terms. High-scoring GO terms are then listed with their associated genes and represent the functional category information of the micro-array experiment. A system called HMDA (Hallym Micro-array Data analysis) is implemented on publicly available micro-array data sets and validated. Our results were also compared with the original analysis.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.9-20
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• 2007

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.

• Entomological Research
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• v.48 no.5
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• pp.390-399
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• 2018

In order to precisely assess gene expression levels, the suitable internal reference genes must be served to quantify real-time reverse transcription polymerase chain reaction (RT-qPCR) data. For armyworm, Mythimna separata, which reference genes are suitable for assessing the level of transcriptional expression of target genes have yet to be explored. In this study, eight common reference genes, including ${\beta}$-actin (${\beta}$-ACT), 18 s ribosomal (18S), 28S ribosomal (28S), glyceraldehyde-3-phosphate (GAPDH), elongation fator-alpha ($EF1{\alpha}$), TATA box binding protein (TBP), ribosomal protein L7 (RPL7), and alpha-tubulin (${\alpha}$-TUB) that in different developmental stages, tissues and insecticide treatments of M. separata were evaluated. To further explore whether these genes were suitable to serve as endogenous controls, three software-based approaches (geNorm, BestKeeper, and NormFinder), the delta Ct method, and one web-based comprehensive tool (RefFinder) were employed to analyze and rank the tested genes. The optimal number of reference genes was determined using the geNorm program, and the suitability of particular reference genes was empirically validated according to normalized HSP70, and MsepCYP321A10 gene expression data. We found that the most suitable reference genes for the different experimental conditions. For developmental stages, 28S/RPL7 were the optimal reference genes, both $RPL7/EF1{\alpha}$ were suitable for experiments of different tissues, whereas for insecticide treatments, $28S/{\alpha}-TUB$ were suitable for normalizations of expression data. In addition, $28S/{\alpha}-TUB$ were the suitable reference genes because they have the most stable expression among different developmental stages, tissues and insecticide treatments. Our work is the first report on reference gene selection in M. separata, and might serve as a precedent for future gene expression studies.

• Journal of KIISE
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• v.43 no.1
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• pp.54-60
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• 2016

Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain's structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.

• BMB Reports
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• v.39 no.4
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• pp.441-447
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• 2006

Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and non-redundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain.

• Genomics & Informatics
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• v.3 no.1
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• pp.39-42
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• 2005

DNA microarray is a high-throughput biomedical technology that monitors gene expression for thousands of genes in parallel. The abundance and complexity of the gene expression data have given rise to a requirement for their systematic management and analysis to support many laboratories performing microarray research. On these demands, we developed Xperanto for integrated data management and analysis using user-friendly web-based interface. Xperanto provides an integrated environment for management and analysis by linking the computational tools and rich sources of biological annotation. With the growing needs of data sharing, it is designed to be compliant to MGED (Microarray Gene Expression Data) standards for microarray data annotation and exchange. Xperanto enables a fast and efficient management of vast amounts of data, and serves as a communication channel among multiple researchers within an emerging interdisciplinary field.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.2
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• pp.641-646
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• 2012

In our recent report on gene expression in gastric cancer we identified the endo-sulfatase Sulf-1 gene to be up-regulated in gastric tumors relative to apparently normal (AN), and paired normal (PN) gastric tissue samples. In the present report we investigate the protein expression levels of Sulf-1 gene in gastric tumors, AN and PN samples using tissue microarray (TMA) and immunohistochemistry. Expression data was collected from two sets of TMA's containing replicate sections of tissue samples. Scoring data from TMA set-1 revealed a significant difference in Sulf-1 immunoreactivity between tumors and "normals" (PN and AN) (p-value = 0.001928). Also, Sulf-1 expression in tumors was also significantly different from either PN (p-value = 0.019) or AN (p-value = 0.006) samples. Similar results were obtained from analysis of scoring data from the second set of arrays. Comparison of mRNA expression and protein expression in gastric tumor tissues revealed that in 6/20 (30%) tumor samples showed up-regulated protein expression concordant with over-expression of mRNA. However, a discord with mRNA being over-expressed relative to down regulated protein expression was observed in majority 14/20 (70%) of tumor samples. Our study indicates down regulation of Sulf-1 protein expression in gastric tumors relative to PN and AN samples which is discordant with mRNA over-expression seen in tumors.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.389-399
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• 2013

This article introduces Gaussian process regression and shows its application with time-course microarray gene expression data. Gene screening for yeast cell cycle microarray expression data is accomplished with a ratio of log marginal likelihood that uses Gaussian process regression with a squared exponential covariance kernel function. Gaussian process regression fitting with each gene is done and shown with the nine top ranking genes. With the screened data the Gaussian model-based clustering is done and its silhouette values are calculated for cluster validity.