• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.340-343
• /
• 2008

유전자들은 복잡한 상호작용을 통해 세포의 기능이 조절된다. 상호작용하는 유전자 그룹들을 유전자 조절 네트워크라고 한다. 기존의 유전자 조절 네트워크는 2D microarray 데이터를 이용하여 시간의 흐름에 따른 유전자간의 상호작용을 알 수가 없었다. 이 논문에서는 시간의 변화에 따른 유전자들 간의 조절관계를 살펴 볼 수 있는 조절네트워크 모델링의 방법을 제시한다. 유전자의 발현양을 표시하기 위해 이진 이산화 방법을 사용하였고 3D microarray 데이터에서 유전자 발현 패턴을 찾기 위해 Cube mining 알고리즘을 적용하였고, 유전자간의 관계를 밝히기 위해 시간 관계 규칙탐사 기법을 사용하여 유전자들 간의 시간 관계를 포함한 유전자 조절네트워크를 구축하였다. 이 연구는 시간의 흐름에 따른 유전자간의 상호작용을 알 수 있으며, 모델링된 조절 네트워크를 이용하여 기능이 아직 발견되지 않은 유전자들의 기능을 예측 할 수 있다.

• The Korean Journal of Applied Statistics
• /
• v.23 no.4
• /
• pp.693-703
• /
• 2010

Diseases of human or economical traits of cattles are occured by interaction of genes. We introduce expanded multifactor dimensionality reduction(E-MDR), dummy multifactor dimensionality reduction(D-MDR) and SNPHarvester which are developed to find interaction of genes. We will select interaction of outstanding gene combinations and select final best genotype groups.

• Journal of KIISE:Computer Systems and Theory
• /
• v.36 no.2
• /
• pp.60-67
• /
• 2009

Analyzing protein-protein interactions(PPI) is an important task in bioinformatics as it can help in new drugs' discovery process. However, due to vast amount of PPI data and their complexity, efficient visualization of the data is still remained as a challenging problem. We have developed efficient and effective visualization system that integrates Gene Ontology(GO) and PPI network to provide better insights to scientists. To provide efficient data visualization, we have employed dynamic interactive graph drawing methods and context-based browsing strategy. In addition, quick and flexible cross-reference system between GO and PPI; LCA(Least Common Ancestor) finding for GO; and etc are supported as special features. In terms of interface, our visualization system provides two separate graphical windows side-by-side for GO graphs and PPI network, and also provides cross-reference functions between them.

• The KIPS Transactions:PartB
• /
• v.14B no.5
• /
• pp.383-390
• /
• 2007

Interactions between proteins and genes are often considered essential in the description of biomolecular phenomena and networks of interactions are considered as an entre for a Systems Biology approach. Recently, many works try to extract information by analyzing biomolecular text using natural language processing technology. Previous researches insist that linguistic information is useful to improve the performance in detecting gene interactions. However, previous systems do not show reasonable performance because of low recall. To improve recall without sacrificing precision, this paper proposes a new method for detection of gene interactions based on syntactic relations. Without biomolecular knowledge, our method shows reasonable performance using only small size of training data. Using the format of LLL05(ICML05 Workshop on Learning Language in Logic) data we detect the agent gene and its target gene that interact with each other. In the 1st phase, we detect encapsulation types for each agent and target candidate. In the 2nd phase, we construct verb lists that indicate the interaction information between two genes. In the last phase, to detect which of two genes is an agent or a target, we learn direction information. In the experimental results using LLL05 data, our proposed method showed F-measure of 88% for training data, and 70.4% for test data. This performance significantly outperformed previous methods. We also describe the contribution rate of each phase to the performance, and demonstrate that the first phase contributes to the improvement of recall and the second and last phases contribute to the improvement of precision.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.2
• /
• pp.277-287
• /
• 2013

It is important to detect the gene-gene interaction in GWAS (genome-wide association study). There have been many studies on detecting gene-gene interaction. The one is D-MDR (dummy multifoactor dimensionality reduction) method. The goal of this study is to evaluate the power of D-MDR for identifying gene-gene interaction by simulation. Also we applied the method on the identify interaction effects of single nucleotide polymorphisms (SNPs) responsible for economic traits in a Korean cattle population (real data).

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.861-869
• /
• 2011

One of the main objects of recent genetic studies is to understand genetic factors that induce complex diseases. If there are interactions between loci, it is difficult to find such associations through a single-locus analysis strategy. Thus we need to consider the gene-gene interactions and/or gene-environment interactions. The MDR(multifactor dimensionality reduction) method is being used frequently; however, it is not appropriate to detect interactions caused by a small fraction of the possible genotype pairs. In this study, we propose a relative risk interaction explorer that detects interactions through the calculation of the relative risks between the control and disease groups from each genetic combinations. For illustration, we apply this method to MDR open source data. We also compare the MDR and the proposed method using the simulated data eight genetic models.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.1
• /
• pp.151-159
• /
• 2013

It is well known that disease of human and economic traits of livestock are affected a lot by gene combination effect rather than a single gene effect. But existing methods have disadvantages such as heavy computing, many expenses and long time. In order to overcome those drawbacks, SNPHarvester was developed to find the main gene combinations among the many genes. In this paper, we used the superior gene combination which are related to the quality of the Korean beef cattle among sets of SNPs by SNPHarvester, and identified the superior genotypes using radial graph which can enhance various qualities of Korean beef among selected SNP combinations.

• The Korean Journal of Applied Statistics
• /
• v.21 no.1
• /
• pp.53-63
• /
• 2008

It is commonly believed that disease of human or economic traits of livestock are caused not by single gene acting alone, but by multiple genes interacting with one an-other. This issue is difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction (MDR) nonparametric statistical method, to improve the identification of single nucleotide polymorphism (SNP) associated with the Hanwoo(Korean cattle) carcass cold weight, is applied and compared with ANOVA results.

• Bioinformatics and Biosystems
• /
• v.1 no.1
• /
• pp.38-45
• /
• 2006

We present a client-server system architecture for inferring genetic interaction networks based on Bayesian networks. It is typical to take tens of hours when genome-wide large-scale genetic interaction networks are inferred in the form of Bayesian networks. To deal with this situation, batch-style distributed system architectures are preferable to interactive standalone architectures. Thus, we have implemented a loosely coupled client-server system for network inference and user interface. The network inference consists of two stages. Firstly, the proposed method divides a whole gene set into overlapped modules, based on biological annotations and expression data together. Secondly, it infers Bayesian networks for each module, and integrates the learned subnetworks to a global network through common genes across the modules.

• Proceedings of the Korea Contents Association Conference
• /
• 2013.05a
• /
• pp.41-42
• /
• 2013

최근 노령화가 급속히 진행되면서 노화에 대한 연구가 활발히 진행되고 있다. 하지만 노화현상은 광범위한 표현형을 지니고 있는 생명현상으로 이에 대한 체계적인 연구를 지원하기 위한 웹포털 사이트가 필요한 실정이다. 특히 노화에 따른 질병과의 연관성 및 관련 유전자에 대한 정보를 수집하고 이를 체계적으로 분석할 수 있는 통합정보시스템은 향후 노화연구를 지원하기 위한 가장 핵심적인 요소라고 할 수 있다. 본 연구에서는 기존 노화와 관련된 461개의 유전자를 기반으로 관련된 질병과의 연관성을 OMIM 데이터베이스를 활용하여 분석하였다. 또한 관련 단백질의 기능을 GO데이터베이스 분석을 통해 유전자의 기능을 분석하였다. Pubmed에서 제공하는 노화관련 논문들의 MeSH 정보 분석을 통해서 노화와 관련된 용어를 분석하였다. 노화와 관련된 64개의 유전자를 키워드로 NCBI의 pubmed 데이터베이스로부터 관련논문을 다운로드 받아 생물학적 상호작용 정보를 추출했다. 생물학적 상호작용은 NCBI에서 제공하는 Metamap 데이터베이스를 기반으로 각각의 생물학적 용어를 정의했다. 현재 노화 유전자 64개에 대해 128,729개의 생물학적 상호작용 정보를 추출했고, 8대 노인성만성질환에 대해 301,176개의 생물학적 상호작용 정보를 추출하였다.