• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.117-126
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• 2007

In this paper, we propose an early rehabilitation training system for the improvement of postural balance with multi-modality on a tilting bed. The integration of the visual, somatosensory and vestibular functions is significant to for maintaining the postural control of the human body. However, conventional rehabilitation systems do not provide multi-modality to trainees. We analyzed the characterization of postural control at different tilt angles of an early rehabilitation training system, which consists of a tilting bed, a visual feedback, a computer interface, a computer, and a force plate. The software that we developed for the system consists of the training programs and the analysis programs. To evaluate the characterization of postural control, we conducted the first evaluation before the beginning of the training. In the following four weeks, 12 healthy young and 5 healthy elderly subjects were trained to improve postural control using the training programs with the tilting bed. After four weeks of training, we conducted the second evaluation. The analysis programs assess (center of pressure) COP moving time, COP maintaining time, and mean absolute deviation of the trace before and after training at different tilt angles on the bed. After 4 weeks, the COP moving time was reduced, the COP maintaining time was lengthened, and the mean absolute deviation of the trace was lowered through the repeated use of vertical, horizontal, dynamic circle movement training programs. These results show that this system improves postural balance and could be applied to clinical use as an effective training system.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.138-149
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• 2004

In this study, we analyzed the gene expression data of Saccharomyces cerevisiae obtained from Holstege et al. 1998 to understand the relationship between expression level and nucleotide sequence of a gene. First, the correlation between gene expression and percent composition of each type of nucleotide was computed. It was observed that nucleotide 'G' and 'C' show positive correlation (r ${\geq}$ 0.15), 'A' shows negative correlation (r ${\approx}$ -0.21) and 'T' shows no correlation (r ${\approx}$ 0.00) with gene expression. It was also found that 'G+C' rich genes express more in comparison to 'A+T' rich genes. We observed the inverse correlation between composition of a nucleotide at genome level and level of gene expression. Then we computed the correlation between dinucleotides (e.g. AA, AT, GC) composition and gene expression and observed a wide variation in correlation (from r = -0.45 for AT to r = 0.35 for GT). The dinucleotides which contain 'T' have wide range of correlation with gene expression. For example, GT and CT have high positive correlation and AT have high negative correlation. We also computed the correlation between trinucleotides (or codon) composition and gene expression and again observed wide range of correlation (from r = -0.45 for ATA r = 0.45 for GGT). However, the major codons of a large number of amino acids show positive correlation with expression level, but there are a few amino acids whose major codons show negative correlation with expression level. These observations clearly indic ate the relationship between nucleotides composition and expression level. We also demonstrate that codon composition can be used to predict the expression of gene in a given condition. Software has been developed for calculating correlation between expression of gene and codon usage.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10847-10853
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• 2015

Background: Lapatinib, a dual tyrosine kinase inhibitor that interrupts the epidermal growth factor receptor (EGFR) and HER2/neu pathways, has been indicated to have significant efficacy in treating HER2-positive breast cancer. However, acquired drug resistance has become a very serious clinical problem that hampers the use of this agent. In this study, we aimed to screen small molecule drugs that might reverse lapatinib-resistance of breast cancer by exploring differentially expressed genes (DEGs) via a bioinformatics method. Materials and Methods: We downloaded the gene expression profile of BT474-J4 (acquired lapatinib-resistant) and BT474 (lapatinib-sensitive) cell lines from the Gene Expression Omnibus (GEO) database and selected differentially expressed genes (DEGs) using dChip software. Then, gene ontology and pathway enrichment analyses were performed with the DAVID database. Finally, a connectivity map was utilized for predicting potential chemicals that reverse lapatinib-resistance. Results: A total of 1, 657 DEGs were obtained. These DEGs were enriched in 10 pathways, including cell cycling, regulation of actin cytoskeleton and focal adhesion associate examples. In addition, several small molecules were screened as the potential therapeutic agents capable of overcoming lapatinib-resistance. Conclusions: The results of our analysis provided a novel strategy for investigating the mechanism of lapatinib-resistance and identifying potential small molecule drugs for breast cancer treatment.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.697-709
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• 2022

Objective : The present study aimed to identify the function of ischemic stroke (IS) patients' peripheral blood and its role in IS, explore the pathogenesis, and provide direction for clinical research progress by comprehensive bioinformatics analysis. Methods : Two datasets, including GSE58294 and GSE22255, were downloaded from Gene Expression Omnibus database. GEO2R was utilized to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using the database annotation, visualization and integrated discovery database. The protein-protein interaction (PPI) network of DEGs was constructed by search tool of searching interactive gene and visualized by Cytoscape software, and then the Hub gene was identified by degree analysis. The microRNA (miRNA) and miRNA target genes closely related to the onset of stroke were obtained through the miRNA gene regulatory network. Results : In total, 36 DEGs, containing 27 up-regulated and nine down-regulated DEGs, were identified. GO functional analysis showed that these DEGs were involved in regulation of apoptotic process, cytoplasm, protein binding and other biological processes. KEGG enrichment analysis showed that these DEGs mediated signaling pathways, including human T-cell lymphotropic virus (HTLV)-I infection and microRNAs in cancer. The results of PPI network and cytohubba showed that there was a relationship between DEGs, and five hub genes related to stroke were obtained : SOCS3, KRAS, PTGS2, EGR1, and DUSP1. Combined with the visualization of DEG-miRNAs, hsa-mir-16-5p, hsa-mir-181a-5p and hsa-mir-124-3p were predicted to be the key miRNAs in stroke, and three miRNAs were related to hub gene. Conclusion : Thirty-six DEGs, five Hub genes, and three miRNA were obtained from bioinformatics analysis of IS microarray data, which might provide potential targets for diagnosis and treatment of IS.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.999-1009
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• 2004

As a new approach to the diagnosis of cancers, bioinformatics attracts great interest these days. Machine teaming techniques have produced valuable results, but the field of medicine requires not only highly accurate classifiers but also the effective analysis and interpretation of them. Since gene expression data in bioinformatics consist of tens of thousands of features, it is nearly impossible to represent their relations directly. In this paper, we propose a method composed of a feature selection method and genetic programming. Rank-based feature selection is adopted to select useful features and genetic programming based arithmetic operators is used to generate classification rules with features selected. Experimental results on Lymphoma cancer dataset, in which the proposed method obtained 96.6% test accuracy as well as useful classification rules, have shown the validity of the proposed method.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.39-49
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• 2004

When there is a lack of detailed kinetic information, dFBA(dynamic flux balance analysis) has correctly predicted cellular behavior under given environmental conditions with FBA and different ial equations. However, until now, dFBA has centered on substrate concentration, cell growth, and gene on/off, but a detailed hierarchical structure of a regulatory network has not been taken into account. For this reason, the dFBA has limited the represen tation of interactions between specific regulatory proteins and genes and the whole transcriptional regulation mechanism with environmental change. Moreover, to calculate optimal metabolic flux distribution which maximizes the growth flux and predict the b ehavior of cell system, linear programming package(LINDO) and spreadsheet package(EXCEL) have been used simultaneously. thses two software package have limited in the visual representation of simulation results and it can be difficult for a user to look at the effects of changing inputs to the models. Here, we descirbes the construction of hierarchical regulatory network with defined symbolsand the development of an integrated system that can predict the total control mechanism of regulatory elements (opero ns, genes, effectors, etc.), substrate concentration, growth rate, and optimal flux distribution with time. All programming procedures were accoplished in a visual programming environment (LabVIEW).

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1565-1573
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• 2018

The recent breakthroughs of multimedia computing have expanded its scope of application to the fields not been touched before. The reproduction of late artists' performance, especially dancers', which had put on the back burner until now, also beginning to shed the light. This study showed how to apply it to the reproduction of these late performers. Through this work, not only did we reduce the working time by applying the software to the manual process which has been dominantly practiced for reproducing the existing stage, but also made it possible to be reused in the subsequent work.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1266-1275
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• 2004

Bioinformatics is concerned with the creation and development of advanced information and computational technologies for problems in biology. It is divided into genomics, proteomics and metabolimics. In metabolimics, an organism is represented by metabolic pathway, i.e., well-displayed graph, and so the graph drawing tool to draw pathway well is necessary to understand it comprehensively. In this paper, we design an improved drawing algorithm. It enhances the readability by making use of the bipartite graph. Also it is possible to draw large graph properly by considering the facts that metabolic pathway graph is scale-free network and is composed of circular components, hierarchic components and linear components.

• Genomics & Informatics
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• v.7 no.2
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• pp.136-140
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• 2009

Genome-wide association (GWA) studies may benefit from the inclusion of imputed SNPs into their dataset. Due to its predictive nature, the imputation process is typically not perfect. Thus, it would be desirable to develop a scheme for filtering out the imputed SNPs by maximizing the concordance with the observed genotypes. We report such a scheme, which is based on the combination of several parameters that are calculated by PLINK, a popular GWA analysis software program. We imputed the genotypes of 8,842 Korean individuals, based on approximately 2 million SNP genotypes of the CHB＋JPT panel in the International HapMap Project Phase II data, complementing the 352k SNPs in the original Affymetrix 5.0 dataset. A total of 333,418 SNPs were found in both datasets, with a median concordance rate of 98.7%. The concordance rates were calculated at different ranges of parameters, such as the number of proxy SNPs (NPRX), the fraction of successfully imputed individuals (IMPUTED), and the information content (INFO). The poor concordance that was observed at the lower values of the parameters allowed us to develop an optimal combination of the cutoffs (IMPUTED${\geq}$0.9 and INFO${\geq}$0.9). A total of 1,026,596 SNPs passed the cutoff, of which 94,364 were found in both datasets and had 99.4% median concordance. This study illustrates a conservative scheme for filtering imputed SNPs that would be useful in GWA studies.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.71-76
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• 2006

Comparative genomic hybridization (CGH) is a technique for studying chromosomal changes in cancer. As cancerous cells multiply, they can undergo dramatic chromosomal changes, including chromosome loss, duplication, and the translocation of DNA from one chromosome to another. Chromosome aberrations have previously been detected using optical imaging of whole chromosomes, a technique with limited sensitivity, resolution, quantification, and throughput. Efforts in recent years to use microarrays to overcome these limitations have been hampered by inadequate sensitivity, specificity and flexibility of the microarray systems. The oligonucleotide CGH microarray system overcomes several scientific hurdles that have impeded comparative genomic studies of cancer. This new system can reliably detect single copy deletions in chromosomes. The system includes a whole human genome microarray, reagents for sample preparation, an optimized microarray processing protocol, and software for data analysis and visualization. In this study, we determined the sensitivity, accuracy and reproducibility of the new system. Using this assay, we find that the performance of the complete system was maintained over a range of input genomic DNA from 5 ug down to 0.15 ug.