• 한국통신학회논문지
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• 제40권10호
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• pp.2054-2061
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• 2015

최근 생물학적으로 영감을 받은 모델링 기술은 단순한 현장 상호작용과 제한된 정보와 함께 이들의 강인성과 확장성, 적응성에 대해 상당한 관심을 받고 있다. 이러한 모델링 기술들 중, 유전자 조절 네트워크(Gene Regulatory Networks)(GRNs)은 세포로부터 생물학적 유기체의 발생과 자연 진화에 대한 이해에서 핵심적인 역할을 하고 있다. 본 논문은 GRN 원리를 무선 센서 네트워크 시스템에 적용하고 시간지연 요건을 충족하는 동시에 에너지 균형을 달성할 수 있는 분산화된 노드 스케쥴링 설계 기법을 제안한다. 각 센서 노드는 소비된 에너지 수준과 지연시간에 반응하여 자동으로 자신의 상태를 스케줄링하며, 이는 GRN 모델에서 영감을 받은 유전자 발현과 단백질 농도 조절 모델에 의해 제어된다. 시뮬레이션 결과는 제안된 방법이 에너지 균형뿐만 아니라 원하는 시간 지연에서 성능을 달성하고 있다는 점을 보여준다.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• 한국유전체학회:학술대회논문집
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• 한국유전체학회 2003년도 The 12th Korea Genome Conference
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• pp.50.2-50.2
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• 2003

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.197-201
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• 2005

When we want to find out the regulatory relationships between genes from gene expression data, dimensionality is one of the big problem. In general, the size of search space in modeling the regulatory relationships grows in O(n$^2$) while the number of genes is increasing. However, hopefully it can be reduced to O(kn) with selected k by applying divide and conquer heuristics which depend on some assumptions about genetic network. In this paper, we approach the modeling problem in divide-and-conquer manner. We applied clustering to make the problem into small sub-problems, then hierarchical model process is applied to those small sub-problems.

• Journal of Microbiology and Biotechnology
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• 제21권2호
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• pp.129-135
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• 2011

Cloning and characterization of the gene encoding a solvent-tolerant protease from the haloalkaliphilic bacterium Geomicrobium sp. EMB2 are described. Primers designed based on the N-terminal amino acid sequence of the purified EMB2 protease helped in the amplification of a 1,505-bp open reading frame that had a coding potential of a 42.7-kDa polypeptide. The deduced EMB2 protein contained a 35.4-kDa mature protein of 311 residues, with a high proportion of acidic amino acid residues. Phylogenetic analysis placed the EMB2 gene close to a known serine protease from Bacillus clausii KSM-K16. Primary sequence analysis indicated a hydrophobic inclination of the protein; and the 3D structure modeling elucidated a relatively higher percentage of small (glycine, alanine, and valine) and borderline (serine and threonine) hydrophobic residues on its surface. The structure analysis also highlighted enrichment of acidic residues at the cost of basic residues. The study indicated that solvent and salt stabilities in Geomicrobium sp. protease may be accorded to different structural features; that is, the presence of a number of small hydrophobic amino acid residues on the surface and a higher content of acidic amino acid residues, respectively.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.43-48
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• 2018

In this paper, more efficient classification result could be obtained by applying the combination of the Hidden Markov Model and SVM Model to HMSV algorithm gene expression data which simulated the stochastic flow of gene data and clustering it. In this paper, we verified the HMSV algorithm that combines independently learned algorithms. To prove that this paper is superior to other papers, we tested the sensitivity and specificity of the most commonly used classification criteria. As a result, the K-means is 71% and the SOM is 68%. The proposed HMSV algorithm is 85%. These results are stable and high. It can be seen that this is better classified than using a general classification algorithm. The algorithm proposed in this paper is a stochastic modeling of the generation process of the characteristics included in the signal, and a good recognition rate can be obtained with a small amount of calculation, so it will be useful to study the relationship with diseases by showing fast and effective performance improvement with an algorithm that clusters nodes by simulating the stochastic flow of Gene Data through data mining of BigData.

• Computers and Concrete
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• 제27권4호
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• 생명과학회지
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• 제19권11호
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• pp.1522-1528
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• 2009

알긴산을 분해하기 위하여 갈조류로부터 분해활성이 있는 해양미생물을 분리하였다. 분리된 균주의 16S ribosomal DNA를 분석한 결과 이전에 보고했던 ALG-5 균주와 비슷한 Streptomyces sp.에 속하는 것으로 나타났다. 상동성이 있는 염기서열로 고안한 특이적인 primer로 PCR을 행함로서 Streptomyces sp. M3의 새로운 alginate lyase 유전자를 클로닝하였다. M3 alginate lyase의 예상 아미노산 서열에는 N-terminal 영역에 YXRSELREM 서열과 C-terimnal 영역에 YFKAGXYXQ 서열이 보존되어 있었다. M3 alginate lyase 단백질의 homology model은 Corynebacterium sp. ALY-1으로부터 얻은 단백질인 alyPG와 같이 $\beta$-jelly roll fold를 main domain으로 가지고 있음이 나타났다. M3 alginate lyase 유전자를 가지는 재조합 E. coli의 세포균질액은 polymannuronate block보다는 polyguluronate block에 대하여 높은 분해력을 가지고 있었다. 아미노산 서열 다중정열 및 homology modeling으로부터 얻은 결과는 M3 alginate lyase가 Family PL-7으로 분류될 수 있음을 말해 준다.

• BMB Reports
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• 제37권1호
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• pp.83-92
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• 2004

As a result of the enormous amount of information that has been collected with E. coli over the past half century (e.g. genome sequence, mutant phenotypes, metabolic and regulatory networks, etc.), we now have detailed knowledge about gene regulation, protein activity, several hundred enzyme reactions, metabolic pathways, macromolecular machines, and regulatory interactions for this model organism. However, understanding how all these processes interact to form a living cell will require further characterization, quantification, data integration, and mathematical modeling, systems biology. No organism can rival E. coli with respect to the amount of available basic information and experimental tractability for the technologies needed for this undertaking. A focused, systematic effort to understand the E. coli cell will accelerate the development of new post-genomic technologies, including both experimental and computational tools. It will also lead to new technologies that will be applicable to other organisms, from microbes to plants, animals, and humans. E. coli is not only the best studied free-living model organism, but is also an extensively used microbe for industrial applications, especially for the production of small molecules of interest. It is an excellent representative of Gram-negative commensal bacteria. E. coli may represent a perfect model organism for systems biology that is aimed at elucidating both its free-living and commensal life-styles, which should open the door to whole-cell modeling and simulation.