• International journal of advanced smart convergence
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• 제5권3호
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.231-235
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• 2004

According to revealing the DNA sequence of human and living things, it increases that a demand on a new computational processing method which utilizes DNA sequence information. In this paper we propose a classification algorithm based on negative selection of the immune system to classify DNA patterns. Negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes n group of antigenic receptor for n different patterns, they can classify into n patterns. In this paper we propose a pattern classification algorithm based on negative selection in nucleotide base level and amino acid level.

• Journal of Microbiology
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• 제38권3호
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• pp.122-131
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• 2000

Phylogenetic classification of the Aphyllophorales was conducted based on the analysis of nuclear small subunit ribosomal RNA (nuc SSU rDNA) sequence. Based on phylogenetic groupings and taxonomic characters, 16 families were recognized and discussed. Although many of the characters had more or less homoplasies, miroscopic characters such ad the mitic system and clamp, spore amyloidity and rot type appeared to be important in the classification of the Aphyllophorales. Phylogenetically significant families were newly defined to improve the classification of the order Aphyllophorales.

• 한국지능시스템학회논문지
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• 제14권5호
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• pp.551-556
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• 2004

인간 및 여러 생물들의 DNA 서열이 밝혀짐에 따라 DNA 서열 정보를 이용할 수 있는 계산적 처리방식에 대한 요구가 늘어나고 있다. 본 논문에서는 DNA의 패턴을 분류할 수 있는 면역계 부정 선택에 기반 한 알고리즘을 제안한다. 부정 선택은 면역세포 생성시 자신을 인식하지 않는 항원 인식부를 생성하기 위한 과정이다. 이 항원 인식부를 통해 자기와 비자기를 구별한다. 이것을 n개의 자기 또는 비자기 집단으로 확장하고 n개의 항원 집단을 구성하면 n개의 패턴 분류가 가능하다. 본 논문에서는 부정 선택에 기반 한 DNA 염기 레벨에서의 패턴 분류방법과 아미노산 레벨에서의 패턴분류 방법을 제안한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.190-195
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• 2004

We propose a pattern classification algorithm using self-nonself discrimination principle of immune cells and apply it to DNA pattern classification problem. Pattern classification problem in bioinformatics is very important and frequent one. In this paper, we propose a classification algorithm based on the negative selection of the immune system to classify DNA patterns. The negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes ${\eta}$ groups of antigenic receptor for ${\eta}$ different patterns, these receptor groups can classify into ${\eta}$ patterns. We propose a pattern classification algorithm based on the negative selection in nucleotide base level and amino acid level. Also to show the validity of our algorithm, experimental results of RNA group classification are presented.

• Genomics & Informatics
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• 제11권4호
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• pp.272-276
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• 2013

Sequence analysis of the 16S rRNA gene has been widely used for the classification of microorganisms. However, we have been unable to clearly identify five Flavobacterium species isolated from a freshwater by using the gene as a single marker, because the evolutionary history is incomplete and the pace of DNA substitutions is relatively rapid in the bacteria. In this study, we tried to classify Flavobacterium species through multilocus sequence analysis (MLSA), which is a practical and reliable technique for the identification or classification of bacteria. The five Flavobacterium species isolated from freshwater and 37 other strains were classified based on six housekeeping genes: gyrB, dnaK, tuf, murG, atpA, and glyA. The genes were amplified by PCR and subjected to DNA sequencing. Based on the combined DNA sequence (4,412 bp) of the six housekeeping genes, we analyzed the phylogenetic relationship among the Flavobacterium species. The results indicated that MLSA, based on the six housekeeping genes, is a trustworthy method for the identification of closely related Flavobacterium species.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권7호
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• pp.525-530
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• 2004

A classifier was constructed by using a generalized regression neural network (GRU) and random generator (RG), which was applied to classify DNA sequences. Three data sets evaluated are eukaryotic and prokaryotic sequences (Data-I), eukaryotic sequences (Data-II), and prokaryotic sequences (Data-III). For each data set, the classifier performance was examined in terms of the total classification sensitivity (TCS), individual classification sensitivity (ICS), total prediction accuracy (TPA), and individual prediction accuracy (IPA). For a given spread, the RG played a role of generating a number of sets of spreads for gaussian functions in the pattern layer Compared to the GRNN, the RG-GRNN significantly improved the TCS by more than 50%, 60%, and 40% for Data-I, Data-II, and Data-III, respectively. The RG-GRNN also demonstrated improved TPA for all data types. In conclusion, the proposed RG-GRNN can effectively be used to classify a large, multivariable promoter sequences.

• Parasites, Hosts and Diseases
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• 제42권3호
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• pp.129-135
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• 2004

We compared the DNA sequences of the genus Metagonimus: M. yokogawai, M. takahashii, and M. miyatai. We obtained 288 D1 ribosomal DNA (rDNA) and mitochondrial cytochrome c oxidase subunit I (mtCOI) fragments from the adult worms by PCR, that were cloned and sequenced. Phylogenetic relationships inferred from the nucleotide sequences of the 28S D1 rDNA and mtCOI gene. M. takahashii and M. yokogawai are placed in the same clade supported by DNA sequence and phylogenie tree analysis in 28S D1 rDNA and mtCOI gene region. The above findings tell us that M. takahashii is closer to M. yokogawai than to M. miyatai genetically. This phylogenetic data also support the nomination of M. miyatai as a separate species.

• BMB Reports
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• 제40권2호
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• pp.135-141
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• 2007

Conjugation of the methyl group at the fifth carbon of cytosines within the palindromic dinucleotide 5'-CpG-3' sequence (DNA methylation) is the best studied epigenetic mechanism, which acts together with other epigenetic entities: histone modification, chromatin remodeling and microRNAs to shape the chromatin structure of DNA according to its functional state. The cancer genome is frequently characterized by hypermethylation of specific genes concurrently with an overall decrease in the level of 5-methyl cytosine, the pathological implication of which to the cancerous state has been well established. While the latest genome-wide technologies have been applied to classify and interpret the epigenetic layer of gene regulation in the physiological and disease states, the epigenetic testing has also been seriously explored in clinical practice for early detection, refining tumor staging and predicting disease recurrence. This critique reviews the latest research findings on the use of DNA methylation in cancer diagnosis, prognosis and staging/classification.

• 한국균학회지
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• 제38권1호
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• pp.40-47
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• 2010

Phytophthora 속의 핵(ypt 유전자, rDNA-IGS region) 및 미토콘드리아(Cox 유전자, $\beta$-tubline 유전자, EF1A 유전자) 내에 존재하는 5가지 유전자 영역을 이용하여 국내 Phytophthora 속 14종의 유전적 다양성을 분석하였다. 국내 Phytophthora 속은 외국의 Phytophthora 속과 동일한 clade를 형성하였으나, 외국의 Phytophthora 속과 마찬가지로 본 연구에서도 분자생물학적 분류와 형태학적 분류와는 연관성을 찾기 어려웠다. 기존에 보고된 국내 P. palmivora KACC 40167 균주의 그룹이 국내에서 보고된 분류체계와 일치하지 않아 추후 재검토가 필요하였다. P. cryptogea-P. drechsleri complex group 내 국내 P. cryptogea KACC 40161 균주와 P. drechsleri KACC 40195 균주는 서로 94% 이상의 유사도를 보여 재동정이 필요하였으며, P. parasitica와 P. nicotianae간의 유사도가 99% 이상으로 나타나 이 두 종간에 통일된 종명이 요구된다. 또한 현재 분자계통학상 5그룹으로 구분된 국내 Phytophthora 속을 외국균주들과 비교하여 10개의 그룹으로 새롭게 재분류하였다. 이러한 결과들은 국내 Phytophthora 속의 유전적 다양성 연구를 위해 유용한 자료가 될 것이다.