• Genomics & Informatics
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• v.1 no.1
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• pp.32-39
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• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.63-68
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• 2005

Transcription factors regulate gene expression by binding to gene upstream region. Each transcription factor has the specific binding site in promoter region. So the analysis of gene upstream sequence is necessary for understanding regulatory mechanism of genes, under a plausible idea that assumption that DNA sequence motif profiles are closely related to gene expression behaviors of the corresponding genes. Here, we present an effective approach to the analysis of the relation between gene expression profiles and gene upstream sequences on the basis of kernel canonical correlation analysis (kernel CCA). Kernel CCA is a useful method for finding relationships underlying between two different data sets. In the application to a yeast cell cycle data set, it is shown that gene upstream sequence profile is closely related to gene expression patterns in terms of canonical correlation scores. By the further analysis of the contributing values or weights of sequence motifs in the construction of a pair of sequence motif profiles and expression profiles, we show that the proposed method can identify significant DNA sequence motifs involved with some specific gene expression patterns, including some well known motifs and those putative, in the process of the yeast cell cycle.

• Molecules and Cells
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• v.42 no.8
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• pp.579-588
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• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.97-115
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• 2001

cDNA microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. Many statistical analysis tools become widely applicable to the analysis of cDNA microarray data. In this talk, we consider a two-way ANOVA model to differentiate genes that have high variability and ones that do not. Using this model, we detect genes that have different gene expression profiles among experimental groups. The two-way ANOVA model is illustrated using cDNA microarrays of 3,800 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.25-29
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• 2005

Microarry 기술의 발전은 유전자의 기능과 상호 관련성 그리고 특성을 파악 가능하게 하였으며, 이를 위한 다양한 분석 기법들이 소개되고 있다. 본 연구에서 소개하는 fuzzy clustering 기법은 genome 영역의 expression 분석에 가장 널리 사용되는 기법중 비지도학습(unsupervized) 분석 기법이다. Fuzzy clustering 기법을 효모(yeast) expression 데이터를 이용하여 분류하여 hard k-means와 비교 하였다.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.2923-2928
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• 2015

Background: Gallbladder carcinoma (GBC) has been stated as an Indian disease, with the highest number of cases being reported from certain districts of northeast India, which has an ethnically distinct population. Unfortunately there are no scientific reports on the underlying molecular mechanisms associated with the pathogenesis of the disease from this region. Aim: The present study evaluated the role of differential expression of human telomerase reverse transcriptase (hTERT) in the development of gall bladder anomalies. Materials and Methods: Blood and tissue samples were collected from patients undergoing routine surgical resection for clinically proven cases of gallbladder disease {cholelithiasis (CL, n=50), cholecystitis (CS, n=40) and GBC (n=30) along with adjacent histopathologically proved non-neoplastic controls (n=15)} with informed consent. Whole blood was also collected from age and sex matched healthy controls (n=25) for comparative analysis. Differential hTERT mRNA expression was evaluated by semi-quantitative rt-PCR and real-time PCR based analysis using ${\beta}$-actin as an internal control. Evaluation of differential hTERT protein expression was studied by Western blot analysis and immunoflourescence. Statistical analysis for differential expression and co-relation was performed by SPSSv13.0 software. Results: Gallbladder anomalies were mostly prevalent in females. The hTERT mRNA and protein expression increased gradiently from normal

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.323-333
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• 2023

Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.261-267
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• 2012

Purpose: The relationship between thymidylate synthase (TS) expression and outcomes in gastric cancer (GC) patients remains controversial, although most studies reported poor survival and reduced response to fluoropyrimidine were related to high TS in tumors. We carried out a systematic review of the literature with meta-analysis to estimate the predictive value of TS expression from published studies. Methods: We indentified 24 studies analysing the outcome data in gastric cancer stratified by TS expression. Effect measures of outcome were hazard ratios (HRs) for overall survival (OS) and event-free survival (EFS), or the odds ratio (OR) for overall response rate (ORR). HRs and ORs from these eligible studies were pooled using random-effects meta-analysis. Results: Fifteen studies investigated outcomes in a total of 844 patients with advanced GC, and nine studies investigated outcomes in a total of 1,235 patients with localized GC undergoing adjuvant therapy. Meta-analysis of estimates showed high TS expression was significantly associated with poor OS in the advanced setting (HR: 1.43, 95%CI: 1.08 - 1.90), and poor EFS in the adjuvant setting (HR: 1.53, 95%CI: 1.01 - 2.32). Subgroup analysis demonstrated TS expression to haves even greater value in predicting OS, EFS and ORR in advanced GC patients treated with fluoropyrimidine monotherapy (HR for OS: 2.32, 95%CI: 1.53 - 3.50; HR for EFS: 1.76, 95%CI: 1.19 - 2.60; OR for ORR: 0.32, 95%CI: 0.11 - 0.95). Conclusion: High levels of TS expression were asssociated with a poorer OS for advanced GC patients compared with low levels. In the adjuvant setting, high TS expression was also associated with a worse EFS. Additional studies with consistent methodology are needed to define the precise predictive value of TS.

• Genomics & Informatics
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• v.17 no.4
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• pp.45.1-45.3
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• 2019

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

• Korean Journal of Plant Resources
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• v.25 no.6
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• pp.693-699
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• 2012

This study was conducted to investigate the effects of Woohwangcheungsimweun (ox bezoar), deer antlers, and wild ginseng on induction of cardiomyocyte differentiation using the established mouse embryonic stem (ES) cells. The expression of atrial natriuretic peptide (ANP) was highest in Woohwangcheungsimweun treatment group. The expression of rabbit anti-GATA-4(GATA-4) and troponin (TnI) were highest in wild ginseng and Woohwangcheungsimweun treatment groups, respectively. Fluorescence activated cell sorting (FACS) analysis showed that the expression of ANP was highest in Dimethyl sulfoxide(DMSO) and Woohwangcheungsimweun treatment groups. The expression of GATA-4 was relatively high in wild ginseng treatment group. The expression of TnI was highest in Woohwangcheungsimweun treatment group. In the gene expression analysis, DMSO greatly inhibited GATA-4 expression to 25% of control. Woohwangcheungsimweun treatment caused to increase cTnI and cardiac ANP expression significantly. Wild ginseng extract upregulated GATA-4 gene expression. In conclusion, DMSO widely used as cardiomyocyte differentiation inducer did not show significant effects on the expression of ANP, GATA-4 and TnI in this study. Woohwangcheungsimweun showed upregulation of ANP and TnI expression. Wild ginseng extract showed greater effects than DMSO on GATA-4 expression. These results might suggest that the combination of Woohwangcheungsimweun and wild ginseng extract treatment can be expected to increase expressions of all three genes.