• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.251-254
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• 2004

Web-Bioconductor System은 유전자 분석에 대한 통계적 모듈과 그래픽 환경을 제공하는 R언어와 DNA chip 데이터의 분석을 수행하는 Bioconductor 패키지를 이용하여 웹으로 DNA chip 데이터를 분석할 수 있도록 설계한 시스템이다. 본 시스템은 DNA chip 데이터의 분석을 위해 사용자 계정 모듈, 데이터 입력 모듈, 전 처리 모듈, 유전자 차등 발현 분석 모듈, 결과 출력 모듈로 구성되어 있으며, 분석된 결과물은 HTML, 이미지, XLS 파일 형태로 제공된다. 웹을 이용하여 DNA chip 분석을 수행함으로써 인터넷이 가능한 곳이면 시간과 장소의 구분이 없이 DNA chip 데이터 분석이 가능하며, 인터넷으로 DNA chip 데이터 분석 자료를 공유할 수 있음으로 연구자들의 상호 의견 교환을 바탕으로 효율적인 분석이 가능할 것이다. 또한 기존의 R언어와 Bioconductor가 전산 지식이 부족한 사람들에게는 접근하기 어려운 점을 웹 인터페이스로 간단하게 구현함으로써 DNA chip 데이터 분석에 있어 용이성과 효율성을 중대하고 있다.

• Genomics & Informatics
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• v.2 no.2
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• pp.92-98
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• 2004

A cDNA microarray experiment is one of the most useful high-throughput experiments in medical informatics for monitoring gene expression levels. Statistical analysis with a cDNA microarray medical data requires a normalization procedure to reduce the systematic errors that are impossible to control by the experimental conditions. Despite the variety of normalization methods, this. paper suggests a more general and synthetic normalization algorithm with a control gene set based on previous studies of normalization. Iterative normalization method was used to select and include a new control gene set among the whole genes iteratively at every step of the normalization calculation initiated with the housekeeping genes. The objective of this iterative normalization was to maintain the pattern of the original data and to keep the gene expression levels stable. Spatial plots, M&A (ratio and average values of the intensity) plots and box plots showed a convergence to zero of the mean across all genes graphically after applying our iterative normalization. The practicability of the algorithm was demonstrated by applying our method to the data for the human photo aging study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3794-3820
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• 2019

In recent years, a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability is much needed. This study presents a cloud storage framework, wherein a 128-bit encryption key has been generated by combining deoxyribonucleic acid (DNA) cryptography and the Hill Cipher algorithm to make the framework unbreakable and ensure a better and secured distributed cloud storage environment. Moreover, the study proposes a DNA-based encryption technique, followed by a 256-bit secure socket layer (SSL) to secure data storage. The 256-bit SSL provides secured connections during data transmission. The data herein are classified based on different qualitative security parameters obtained using a specialized fuzzy-based classification technique. The model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers. A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision-making (MCDM) model has been employed for this, which can decide on the set of suitable storage servers on which the data must be stored and results in a reduction in execution time by keeping up the level of security to an improved grade.

• Molecules and Cells
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• v.25 no.2
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

• BMB Reports
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• v.29 no.1
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• pp.1-10
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• 1996

The binding interaction of ethidium to a series of synthetic deoxyoligonucleotides containing a B-Z junction between left-handed Z-DNA and right-handed B-DNA, was studied. The series of deoxyoligonucleotides was designed so as to vary a dinucleotide step immediately adjacent to a B-Z junction region. Ethidium binds to the right-handed DNA forms and hybrid B-Z forms which contain a B-Z junction, in a highly cooperative manner. In a series of deoxyoligonucleotides, the binding affinity of ethidium with DNA forms which were initially hybrid B-Z forms shows over an order of magnitude higher than that with any other DNA forms, which were entirely in B-form DNA The cooperativity of binding isotherms were described by an allosteric binding model and by a neighbor exclusion model. The binding data were statistically compared for two models. The conformation of allosterically converted DNA forms under binding with ethidium is found to be different from that of the initial B-form DNA as examined by CD spectra. The ratio of the binding constant was interestingly correlated to the free energy of base unstacking and the conformational conversion of the dinucleotide. The more the base stacking of the dinucleotide is unstable, or the harder the conversion of B to A conformation, the higher the ratio of the binding constant of ethidium with the allosterically converted DNA forms and with the initial B-Z hybrid forms. DNA sequence around a B-Z junction region affects the binding affinity of ethidium. The results in this study demonstrate that ethidium could preferentially interact with unusual DNA structures.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.41-44
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• 2000

With the advent of DNA microarray and "chip" technologies, gene expression in an organism can be monitored on a genomic scale, allowing the transcription levels of many genes to be measured simultaneously. Functional interpretation of massive expression data and linking such data to DNA sequences have become the new challenges to bioinformatics. I will us yeast cell cycle expression data analysis as an example to demonstrate how special database and computational methods may be used for extracting functional information, I will also briefly describe a novel clustering algorithm which has been applied to the cell cycle data.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Korean Journal of Plant Resources
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• v.22 no.3
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• pp.195-202
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• 2009

Schisandra chinensis have been traditionally used in Asia for the treatment of dyspnea, cough, mouth dryness, spontaneous diaphoresis, nocturnal diaphoresis, nocturnal emission, dysentery, insomnia and amnesia. The purpose of this study is to evaluate the protective effects of Schisandra chinensis on oxidative DNA damage and lipid peroxidation induced by ROS in non cellular and cellular system. DPPH radical, hydroxyl radical and hydrogen peroxide scavenging assay were used to measure the antioxidant activities. Phi X-174RF I plasmid DNA cleavage assay and intracellular DNA migration assay were used to evaluate the protective effect on oxidative DNA damage. MTT assay and lipid peroxidation assay were used for evaluating the protective effect on oxidative cell damage. It was found to scavenge DPPH radical, hydrogen peroxide and hydroxyl radical and it inhibited oxidative DNA damage, lipid peroxidation and cell death induced by hydroxyl radical. These data indicate that Schisandra chinensis possesses a spectrum of antioxidant and DNA-protective properties

• BMB Reports
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• v.46 no.4
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• pp.225-229
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• 2013

Methylglyoxal (MG) is an endogenous metabolite which is present in increased concentrations in diabetics and reacts with amino acids to form advanced glycation end products. In this study, we investigated whether ferritin enhances DNA cleavage by the reaction of MG with lysine. When plasmid DNA was incubated with MG and lysine in the presence of ferritin, DNA strand breakage was increased in a dose-dependent manner. The ferritin/MG/lysine system-mediated DNA cleavage was significantly inhibited by reactive oxygen species (ROS) scavengers. These results indicated that ROS might participate in the ferritin/MG/lysine system-mediated DNA cleavage. Incubation of ferritin with MG and lysine resulted in a time-dependent release of iron ions from the protein molecules. Our data suggest that DNA cleavage caused by the ferritin/MG/lysine system via the generation of ROS by the Fenton-like reaction of free iron ions released from oxidatively damaged ferritin.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.151-163
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• 2001

- Overview of Bioinformatics and vision of Samsung SDS on it - Overview of Bio Chip and its market - Product roadmap with "Expert system for DNA chip data " - "UniBIO "as an integrated package of DNA chip data analysis - Demo of UniBIO