• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.2337-2340
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• 2002

본 연구는 생물정보학(Bioinformatics)의 가장 기초적인 분야중 하나인, 새롭게 밝혀진 유전자 서열과 이미 밝혀진 유전자 서열 사이의 유사성(similarity)이나 상동성(homology)을 찾기 위한 방법에 대한 연구 중 지역 서열정렬로 사용하는 알고리즘인 Smith-Waterman 알고리즘이 갖고 있는 문제를 파악한다. 긴 서열에 대한 선호를 막고 대신 부분적인 지역 정렬을 다수 개 찾아 정렬시키는 알고리즘을 제안하기로 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.124-126
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• 2003

생물정보학에서 사용되는 많은 프로그램들은 데이터베이스로 부터 방대한 양의 데이터를 검색하고 처리한다. 이러한 환경에서 사용자의 요청마다 데이터베이스를 검색하는 경우 사용자들의 대기 시간이 길어지고 시스템 용량을 초과한다. 이러한 데이터베이스 액세스의 문제점을 해결하기 위하여 카플 기법과 그룹핑 기법이 제안되었다. 본 논문에서는 카플 기법과 그룹핑 기법을 이용하여 유전자 서열 비교 프로그램인 Fasta를 구현하였고 사용자 응답시간을 측정하여 프로그램의 성능을 높일 수 있음을 확인하였다.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.77-85
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• 2011

It is believed that the anomalies or diseases of human organs are identified by the analysis of the patterns. This paper proposes a new classification technique for the identification of cancer disease using the proteome patterns obtained from two-dimensional polyacrylamide gel electrophoresis(2-D PAGE). In the new classification method, three different classification methods such as support vector machine(SVM), multi-layer perceptron(MLP) and k-nearest neighbor(k-NN) are extended by multi-boosting method in an array of subclassifiers and the results of each subclassifier are merged by ensemble method. Genetic algorithm was applied to obtain optimal feature set in each subclassifier. We applied our method to empirical data set from cancer research and the method showed the better accuracy and more stable performance than single classifier.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.682-692
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• 2006

Searching for gene products which have similar biological functions are crucial for bioinformatics. Modern day biological databases provide the functional description of gene products using Gene Ontology(GO). In this paper, we propose a technique for semantic similarity search for gene products using the GO annotation information. For this purpose, an information-theoretic measure for semantic similarity between gene products is defined. And an algorithm for semantic similarity search using this measure is proposed. We adapt Fagin's Threshold Algorithm to process the semantic similarity query as follows. First, we redefine the threshold for our measure. This is because our similarity function is not monotonic. Then cluster-skipping and the access ordering of the inverted index lists are proposed to reduce the number of disk accesses. Experiments with real GO and annotation data show that GORank is efficient and scalable.

• Journal of the Korean Society of Visualization
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• v.7 no.2
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• pp.47-55
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• 2010

In this study, we optimized the shape of the surface aerator that will be installed in a biological reactor using the response surface method. Response surfaces of mass flow rate, impeller torque, mass flow rate per impeller torque are generated and used to track the optimum shape of the aerator. MOGA(Multi-Objective Genetic Algorithm)method is adopted to find the optimum results. By increasing the mass flow rate per impeller torque, increase of oxygen supply efficiency to a reactor is anticipated. To verify the usability of the surface aerator, PIV measurements on flow fields inside a scale-downed biological reactor model are carried out.

• Proceedings of the Korean Society of Embryo Transfer Conference
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• 2004.10a
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• pp.74-74
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• 2004

• Proceedings of the Korean Society of Life Science Conference
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• 2003.05a
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• pp.109-109
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• 2003

• Korean journal of applied entomology
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• v.58 no.1
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• pp.1-8
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• 2019

The genus Nipponopsyche Yazaki is reported from Korea with the species, N. fuscescens Yazaki for the first time. Adult including genitalia, larva, and pupa of the species are redescribed, and DNA barcode for precise identification of the species is also provided.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.980-984
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• 2010

Exploring microRNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. Recently, miRNAs have been discovered as important regulators that play a major role in various cellular processes. Therefore, it is essential to identify functional interactions between miRNAs and mRNAs for understanding the context- dependent activities of miRNAs in complex biological systems. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method, termed layered hypernetworks (LHNs), for identifying functional miRNA-mRNA interactions from heterogeneous expression data. In experiments, we apply the LHN model to miRNA and mRNA expression profiles on multiple cancers. The proposed method identifies cancer-specific miRNA-mRNA interactions. We show the biological significance of the discovered miRNA- mRNA interactions.