• Genomics & Informatics
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• 제7권3호
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• pp.141-147
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• 2009

Developed proteome-scale ortholog and paralog prediction methods are mainly based on sequence similarity. However, it is known that even the closest BLAST hit often does not mean the closest neighbor. For this reason, we added conserved interaction information to find orthologs. We propose a genome-scale, automated ortholog prediction method, named OrthoInterBlast. The method is based on both sequence and interaction similarity. When we applied this method to fly and yeast, 17% of the ortholog candidates were different compared with the results of Inparanoid. By adding protein-protein interaction information, proteins that have low sequence similarity still can be selected as orthologs, which can not be easily detected by sequence homology alone.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권11호
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• pp.803-811
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• 2010

단백질의 세포내 위치와 단백질의 기능은 연관성이 크므로, 단백질의 세포내 위치 예측을 통해서 그 기능에 대한 정보를 얻을 수 있다. 예측 정확도를 높이기 위해서 아미노산 서열 정보이외의 외부 정보들을 효과적으로 이용하려는 연구가 활발하다. 본 논문에서는 아미노산 서열 유사성, 단백질 프로파일, 유전자 온톨로지, 모티프, 문헌 정보에 내재된 세포내 위치 예측 능력을 비교한다. 단백질간의 서열 유사성이 80% 이하인 PLOC 자료를 사용한 실험에서는 서열 유사성과 유전자 온톨로지를 이용하는 방법이 효과적이며, 94.8%의 예측정확도를 얻었다. 단백질 서열간의 유사성이 30% 이하로서 단백질간의 서열 유사성이 작은 BaCelLo IDS 자료는 유전자 온톨로지를 사용하는 것이 효과적이었고, 동물은 93.2%, 곰팡이는 86.6%의 예측정확도로 크게 향상된 성능을 얻었다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.28-31
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• 2000

We describe a hidden Markov model, HMMTIR, for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike the linear HMMs used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The model extends the I-sites library by describing the adjacencies of different sequence-structure motifs as observed in the database, and achieves a great reduction in parameters by representing overlapping motifs in a much more compact form. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model, predicts secondary structure with an accuracy of 74.6% and backbone torsion angles better than any previously reported method, and predicts the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction. HMMSTR has been incorporated into a public, fully-automated protein structure prediction server.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.237-242
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• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.

• 정보처리학회논문지D
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• 제10D권2호
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• pp.261-266
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• 2003

단백질은 아미노산의 선형 중합체(linear polymer)로서 생체의 조직을 구성하고 각종 생화학 반응을 조절하는 역할을 하는 가장 중요한 생체 분자에 속한다. 이러한 단백질의 특성과 기능은 해당 단백질을 구성하는 아미노산의 서열에 의해 결정되기 때문에, 주어진 단백질의 서열을 알아내는 것은 단백질 기능 연구의 출발점이다. 본 논문은 기존의 생화학적 단백질 서열 결정 방법의 단점을 극복할 수 있는 데이터 마이닝 기반 단백질 서열 예측 기법을 제안한다. 복수개의 단백질 절단효소(protease)를 적용함으로써, 서로 중첩된 단백질 조각을 얻어내고, 각 조각의 질량 정보와 단백질 데이타베이스를 이용하여 후보 서열을 식별한다. 얻어진 후보 서열의 조립을 통해 전체 서열을 결정하기 위한, 다중 분할 그래프(multi-partite graph) 구축 및 경로 탐색 기법을 제안한다. 아울러, 대표적인 단백질 서열 데이타베이스인 SWISS-PROT을 이용한 실험을 통해 제안한 방법의 성능을 평가한다.

• 미생물학회지
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• 제39권2호
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• pp.67-75
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• 2003

비교유전체학의 주요 주제 중 유전자서열을 분류하고 단백질기능을 예측하는 연구가 있으며, 이를 위해 단백질 구조, 공통서열 및 바인딩 위치 예측등의 방법과 함께, 전유전체 서열에서 구해지는 유사도 그래프를 분석해 상동유전자를 검색하는 계산학적인 접근방법이 있다. 유사도그래프를 사용한 방법은 서열에 대한 기존 지식에 의존하지 않는 장점이 있지만 유사도 하한값과 같은 주관적인 임계값이 필요한 단점이 있다. 본 논문에서는 반복적으로 그래프를 분해하는 이전의 방법을 일반화시켜, 유사도 그래프에 기반한 유전자 서열군집분석 방법론과 객관적이고 안정적인 파라미터 임계값 계산 방법을 제안한다. 제시된 방법으로 알려진 미생물 유전체 서 열을 분석하여 이전의 방법인 BAG 알고리즘 결과와 비교했다.

• Genomics & Informatics
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• 제6권4호
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• pp.166-172
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• 2008

A multitude of protein-coding sequence variations (CVs) in the human genome have been revealed as a result of major initiatives, including the Human Variome Project, the 1000 Genomes Project, and the International Cancer Genome Consortium. This naturally has led to debate over how to accurately assess the functional consequences of CVs, because predicting the functional effects of CVs and their relevance to disease phenotypes is becoming increasingly important. This article surveys and compares variation databases and in silico prediction programs that assess the effects of CVs on protein function. We also introduce a combinatorial approach that uses machine learning algorithms to improve prediction performance.

• 한국생물물리학회:학술대회논문집
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• 한국생물물리학회 2003년도 정기총회 및 학술발표회
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• pp.65-65
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• 2003

After many genome projects, algorithms and software to process explosively growing biological information have been developed. To process huge amount of biological information, high performance computing equipments are essential. If we use the remote resources such as computing power, storages etc., through a Grid to share the resources in the Internet environment, we will be able to obtain great efficiency to process data at a low cost. Here we present the performance improvement of the protein secondary structure prediction (PSIPred) by using the Grid platform, distributing protein sequence data on the Grid where each computer node analyzes its own part of protein sequence data to speed up the structure prediction. On the Grid, genome scale secondary structure prediction for Mycoplasma genitalium, Escherichia coli, Helicobacter pylori, Saccharomyces cerevisiae and Caenorhabditis slogans were performed and analyzed by a statistical way to show the protein structural deviation and comparison between the genomes. Experimental results show that the Grid is a viable platform to speed up the protein structure prediction and from the predicted structures.

• International Journal of Advanced Culture Technology
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• 제10권2호
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• pp.201-212
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• 2022

With the development of sequencing technology, there is a need for technology to predict the function of the protein sequence. Enzyme Commission (EC) numbers are becoming markers that distinguish the function of the sequence. In particular, many researchers are researching various methods of predicting the EC numbers of protein sequences based on deep learning. However, as studies using various methods exist, a problem arises, in which the exact prediction result of the sequence is unknown. To solve this problem, this paper proposes an All Enzyme Commission (AEC) algorithm. The proposed AEC is an algorithm that executes various prediction methods and integrates the results when predicting sequences. This algorithm uses duplicates to give more weights when duplicate values are obtained from multiple methods. The largest value, among the final prediction result values for each method to which the weight is applied, is the final prediction result. Moreover, for the convenience of researchers, the proposed algorithm is provided through the AllEC web services. They can use the algorithms regardless of the operating systems, installation, or operating environment.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (B)
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• pp.226-228
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• 2005

Predicting the cellular location of an unknown protein gives valuable information for inferring the possible function of the protein. For more accurate Prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting . The overall prediction accuracy evaluated by the 5-fold cross-validation reached $88.53\%$ for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful forpredicting subcellular localization of proteins.