• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.183-187
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• 2002

DNA microarray는 분자생물학 및 DNA 컴퓨팅 분야에 널리 사용되고 있는 실험 도구이다. DNA microarray를 이용하는 한 예는 알려진 유전자 집합을 바탕으로 하여 hybridization을 통해 새로운 DNA 서열을 분석하는 것이다. 이를 위한 가장 간단한 방법은 알려진 유전자의 모든 서열을 DNA microarray 상에 올려놓는 것이지만 이는 결과의 정확도 및 칩 제작비용 면에서 비효율적이다. 따라서 일반적으로는 유전자 서열 정보를 파악한 후 일련의 DNA 서열을 선택하는 probe 디자인 과정을 거친다. 그러나 현재 유전자 서열을 바탕으로 최적의 probe 집합을 찾는 결정적인 방법이 존재하고 있지 않다. 이에 본 논문은 oligo DNA microarray을 이용한 DNA 서열 분석 문제에 있어서 가능한 많은 유전자를 인식하면서 최소의 probe 개수를 갖는 집합을 찾는 방법을 제안한다. 제시된 방법은 가능한 probe 집합들로 해집합을 구성한 후, 유전알고리즘을 이용한 진화 과정을 통해 목적하는 probe 집합을 찾는다. 본 논문에서는 GenBank로부터 얻은 일련의 유전자 집합을 대상으로 실험하였으며 그 결과를 분석하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.19-26
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• 2008

Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene sets showing significant differences between two groups of microarray expression profiles and simultaneously uncover their biological meanings in an elegant way by employing gene annotation databases, such as Cytogenetic Band, KEGG pathways, gene ontology, and etc. For the gone set enrichment analysis, all the genes in a given dataset are first ordered by the signal-to-noise ratio between the groups and then further analyses are proceeded. Despite of its impressive results in several previous studies, however, gene ranking by the signal-to-noise ratio makes it difficult to consider highly up-regulated genes and highly down-regulated genes at the same time as the candidates of significant genes, which possibly reflect certain situations incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the gene set enrichment analysis method with Fisher criterion for gene ranking and also evaluate its effects in Leukemia related pathway analyses.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.241-251
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• 2018

The analysis of gene sets in microarray has advantages in interpreting biological functions and increasing statistical powers. Many statistical methods have been proposed for detecting significant gene sets that show relations between genes and phenotypes, but there is no consensus about which is the best to perform gene sets analysis and permutation based tests are considered as standard tools. When many gene sets are tested simultaneously, a large number of random permutations are needed for multiple testing with a high computational cost. In this paper, several parametric approximations are considered as alternatives of the permutation distribution and the moment based gene set test has shown the best performance for providing p-values of the permutation test closely and quickly on a general framework.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.1-8
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• 2010

Recently lots of attention has been paid to gene set analysis for identifying differentially expressed gene-sets between two sample groups. Unlike earlier approaches, the gene set analysis enables us to find significant gene-sets along with their functional characteristics. For this reason, various novel approaches have been suggested lately for gene set analysis. As one of such, PAGE is a parametric approach that employs average difference (AD) as an expression metric to quantify expression differences between two sample groups and assumes that the distribution of gene scores is normal. This approach is preferred to non-parametric approach because of more effective performance. However, the metric AD does not reflect either gene expression intensities or variances over samples in calculating gene scores. Thus, in this paper, we investigate the usefulness of several other expression metrics for parametric gene-set analysis, which consider actual expression intensities of genes or their expression variances over samples. For this purpose, we examined three expression metrics, WAD (weighted average difference), FC (Fisher's criterion), and Abs_SNR (Absolute value of signal-to-noise ratio) for parametric gene set analysis and evaluated their experimental results.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.327-338
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• 2009

A microarray dataset is 2-dimensional dataset with a set of genes and a set of conditions. A bicluster is a subset of genes that show similar behavior within a subset of conditions. Genes that show similar behavior can be considered to have same cellular functions. Thus, biclustering algorithm is a useful tool to uncover groups of genes involved in the same cellular process and groups of conditions which take place in this process. We are proposing a polynomial time algorithm to identify functionally highly correlated biclusters. Our algorithm identifies 1) the gene set that has hidden patterns even if the level of noise is high, 2) the multiple, possibly overlapped, and diverse gene sets, 3) gene sets whose functional association is strongly high, and 4) deterministic biclustering results. We validated the level of functional association of our method, and compared with current methods using GO.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.17-24
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• 2010

Recently the study of identifying bio-markers for disease diagnosis and prognosis has been actively performed. In particular, lots of attentions have been paid to the finding of pathway gene-sets differentially expressed in disease patients rather than the finding of individual gene markers. In this paper we propose a novel method to identify disease-related pathway gene-sets based on time-series microarray data. For this purpose, we firstly compute individual gene scores by the using maSigPro (microarray Significant Profiles) and then arrange all the genes in the decreasing order of the corresponding gene scores. The rank of each gene in the entire list is used to evaluate the statistical significance of candidate gene-sets with Wilcoxson rank sum test. For the generation of candidate gene-sets, MSigDB (Molecular Signatures Database) pathway information has been employed. The experiment was conducted with prostate cancer time-series microarray data and the results showed the usefulness of the proposed method by correctly identifying 6 out of 7 biological pathways already known as being actually related to prostate cancer.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.545-549
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• 2009

Microarray data sets could contain thousands of gene expression levels and have been considered as an important source from which meaningful patterns could be extracted for further analysis in biological studies. It is sometimes necessary to retrieve out specific genes or samples of analyst's interest in an effective way. This paper is concerned with a method to make use of fuzzy signature set in order to filter out genes or samples which satisfy complicated constraints as well as simple ones. Fuzzy signatures are an extension of vector valued fuzzy sets, in which elements of the vector are allowed to have a vector. Fuzzy signature sets are similar to fuzzy signatures except that their leaf elements are fuzzy sets defined on the interval [0,1]. This paper introduces an extension of fuzzy signature sets which specifies aggregation operators at each internal node and comparison operators for aggregation. It also shows how to use the extended fuzzy signature sets in microarray data retrieval and some examples of its usage.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.145-148
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• 2007

생명과학분야에서 마이크로어레이 기술은 세포에서의 RNA 발현 프로파일을 관찰할 수 있도록 함으로써 생명현상의 규명 및 약물개발 둥에서 분자수준의 생명현상에 대한 관찰과 분석이 가능 해지고 있다. 마이크로어레이 데이터분석에서는 특정한 처리나 과정에서 현저한 특성을 보이는 유전자를 식별하기 위한 분석뿐만 아니라 유전자 전체인 게놈수준에서의 분석도 이루어진다. 최근 유전자의 발현이 다양한 조절, 신호전달 및 대사경로에 의해서 영향을 받고 있다는 관점에서 게놈수준의 분석에 관심이 증가하고 있다. 약물반응 실험에서는 약물에 대한 게놈수준의 발현 프로파일을 관찰하는 것도 많은 정보를 제공할 수 있다. 약물실험에서는 대조군과 실험군들간에 관심 있는 상대적인 발현특성을 갖는 유전자군을 전체적으로 추출하는 것이 필요한 경우가 있다. 예를 들면 정상군은 두개의 실험군에 대해서 중간청도의 발현정도를 갖는 유전자군을 식별하는 분석을 하는 경우, 생물학적인 데이터의 특성상 절대값을 비교하는 방법으로는 유용한 유전자들을 효과적으로 식별해 낼 수 없다. 이 논문에서는 정상군과 실험군들의 발현정도값의 경향을 판단하기 위해서 각 유전자에 대해서 집단별 대표값을 선정하여 퍼지집합으로 집단의 값의 범위를 결정하고, 이를 이용하여 특정 패턴을 갖는 유전자들을 식별해내는 방법을 제안하고, 실제 데이터를 통해서 실험한 결과를 보인다.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1795-1801
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• 2018

Genome-Wide Association Study (GWAS) have been used to identify susceptibility genes for complex human diseases and many recent studies succeed to report common genetic factors for various diseases. Unfortunately, it is hard to understand all biological functions and mechanisms around the complex disease with GWAS only although the number of known associated genes with diseases is increased drastically because GWAS is a single locus based approach while not a gene but numerous factors may affect a disease associated pathways. PRaDA generates a combined report with genes, pathways and Gene Ontology (GO) using single nucleotide polymorphism (SNP) analysis output. The PRaDA reports not only directly associated pathways but also functionally related ones for identifying accumulated effects of low p-value SNPs. Through integrated information including indirect functional effects, user could have insights of overall disease mechanisms and markers.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.1
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• pp.90-100
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• 1999

유전자 알고리즘은 전통적인 등반 알고리즘을 이용하여 구하기 어려웠던 최적화 문제를 해결하기 위한 강인한(Robust) 탐색 기법이다. 특히 목적함수가 (1)여러 개의 국부 최대치를 가지는 경우, (2)수학적으로 표현이 불가능하거나 어려운 경우, (3)목적함수에 교란 항(disturbance term)이 섞여 있을 경우도 우수한 탐색 능력을 갖는 것으로 알려져 있다. 본 논문에서는 유전자 알고리즘을 이용하여 나타나는 다양한 해집합을 형성하는 개체군을 군집성 분석(cluster analysis)을 이용하여 군집화하고, 각 군집에 부여된 군집 적합도에 따라서 최적해를 구함으로써 단순 유전자 알고리즘에 의한 최적화보다 훨씬 향상된 탐색 알고리즘을 제안하였다. 반응표면의 형태가 정형화한 테스트 함수의 형태로 나타난다고 가정한 경우에 대하여 몬테 칼로 시뮬레이션을 통하여 본 알고리즘을 적용하여 평가하고 분석하였다.