• Journal of Microbiology
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• v.44 no.2
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• pp.137-144
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• 2006

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.936-943
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• 2002

The Simulated Annealing (SA) algorithm has been successfully applied to various difficult combinatorial optimization problems. Since the performance of a SA algorithm, generally depends on values of the parameters, it is important to select the most appropriate parameter values. In this paper the SA algorithm is optimally designed for performance acceleration, by using the Taguchi method. Several test problems are solved via the SA algorithm optimally designed, and the solutions obtained are compared to solution results McMullen & Frazier(2000). The performance of the SA algorithm is evaluated in terms of solution quality and computation times. Computational results show that the proposed SA algorithm is effective and efficient in finding near-optimal solutions.

• Proceedings of the Botanical Society of Korea Conference
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• 1987.07a
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• pp.323-347
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• 1987

The R locus of maize in one of several genes that regulate the anthocyanin pigments throughout the body of the plant and seed. The R gene product may regulate pigment deposition by controlling the expression of the flavonoid biosynthetic gene pathway in a tissue-specific manner. To understand the basis for tissue specific regulation and allelic variation at R, the molecular study has been done by cloning a portion of the R complex by transposon tagging with Ac. R specific probe were cloned from the R-nj mutant induced by Ac insertion mutagenesis. From southern analysis of R-r complex using the R-nj probe, the structure of R-r was proposed that R-r containes the three elements, (P)(Q)(S). These elements may organize as the inversion triplication model which (S) sequence was inverted in relation to (P) and (Q). The R-sc derivated from R-mb or R-nj was cloned with R-nj probe, and molecular genetical data showed that R-sc containes tissue specific and tissue nonspecific area, and the sequencing of R-sc are progressed now.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.984-988
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• 2005

To accomplish various and complex tasks by intelligent robots, improvement is needed not only in mechanical system architecture but also in control system architecture. Hybrid control architecture has been suggested as a mutually complementing architecture of the weak points of a deliberative and a reactive control. This paper addresses a control architecture of robots, and a behavior representation methodology. The suggested control architecture consists of three layers of deliberative, sequencing, and reactive as hybrid control architecture. Multi-layer behavior model is employed to represent desired tasks. 3D simulation will be conducted to verify the applicability of suggested control architecture and behavior representation method.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.60-65
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• 2013

Introducing the concept of construction safety in the design/engineering phase can improve the efficiency and effectiveness of safety management on construction sites. In this sense, further improvements for safety can be made in the design/engineering phase through the development of (1) an automated hazard identification process that is little dependent on user knowledge, (2) an automated construction schedule generation to accommodate varying hazard information over time, and (3) a visual representation of the results that is easy to understand. In this paper, we formulate an automated hazard identification framework for construction safety by extracting hazard information from related regulations to eliminate human interventions, and by utilizing a visualization technique in order to enhance users' understanding on hazard information. First, the hazard information is automatically extracted from textual safety and health regulations (i.e., Occupational Safety Health Administration (OSHA) Standards) by using natural language processing (NLP) techniques without users' interpretations. Next, scheduling and sequencing of the construction activities are automatically generated with regard to the 3D building model. Then, the extracted hazard information is integrated into the geometry data of construction elements in the industry foundation class (IFC) building model using a conformity-checking algorithm within the open source 3D computer graphics software. Preliminary results demonstrate that this approach is advantageous in that it can be used in the design/engineering phases of construction without the manual interpretation of safety experts, facilitating the designers' and engineers' proactive consideration for improving safety management.

• Journal of Genetic Medicine
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• v.20 no.2
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• pp.39-45
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• 2023

As advanced sequencing technologies continue to uncover an increasing number of variants in genes associated with human genetic diseases, there is a growing demand for systematic approaches to assess the impact of these variants on human development, health, and disease. While in silico analyses have provided valuable insights, it is essential to complement these findings with model organism studies to determine the functional consequences of genetic variants in vivo. Drosophila melanogaster is an excellent genetic model for such functional studies due to its efficient genetic technologies, high gene conservation with humans, accessibility to mutant fly resources, short life cycles, and cost-effectiveness. The traditional GAL4-UAS system, allowing precise control of gene expression through binary regulation, is frequently employed to assess the effects of monoallelic variants. Recombinase medicated cassette exchange or CRISPR-Cas9-mediated GAL4 insertion within coding introns or substitution of gene body with Kozak-Gal4 result in the loss-of-function of the target gene. This GAL4 insertion strategy also enables the expression of reference complementary DNA (cDNA) or cDNA carrying genetic variants under the control of endogenous regulatory cis elements. Furthermore, the CRISPR-Cas9-directed tissue-specific knockout and cDNA rescue system provides the flexibility to investigate candidate variants in a tissue-specific and/or developmental-timing dependent manner. In this review, we will delve into the diverse genetic techniques available in Drosophila and their applications in diagnosing and studying numerous undiagnosed diseases over the past decade.

• Microbiology and Biotechnology Letters
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• v.52 no.2
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• pp.141-151
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• 2024

Isochrysis sp. is a sea microalga that has become a species of interest because of the extreme lipid content and rapid growth rate of this organism indicating its potential for efficient biofuel production. Using genome sequencing/genome-scale modeling for the prediction of Isochrysis sp. metabolic utilities there is high scope for the identification of essential pathways for the extraction of byproducts of interest at a higher rate. In our work, we design and present iIsochr964, a genome-scale metabolic model of Isochrysis sp. including 4315 reactions, 934 genes, and 1879 metabolites, which are distributed among fourteen compartments. For model validation, experimental culture, and isolation of Isochrysis sp. were performed and biomass values were used for validation of the genome-scale model. OptFlux was instrumental in uncovering several novel metabolites that influence the organism's metabolism by increasing the flux of interacting metabolites, such as Malonyl-CoA, EPA, Protein and others. iIsochr964 provides a compelling resource of metabolic understanding to revolutionize its industrial applications, thereby fostering sustainable development and allowing estimations and simulations of the organism metabolism under varying physiological, chemical, and genetic conditions. It is also useful in principle to provide a systemic view of Isochrysis sp. metabolism, efficiently guiding research and granting context to omics data.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.659-667
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• 2009

ADL SCORM(Sharable Content Object Reference Model) has been widely accepted as a global reference model for standardizing e-learning technology, and SCORM 2004 4th Edition, a stable version of SCORM, gives content developers the efficient way to build interoperable and reusable e-learning contents. Recently, a number of research efforts have been taken to build on-line SCORM contents based on some traditional training or learning styles. However, they have lacked for supporting more sophisticated learning style such as game based learning, and especially they do not consider employing the specific components of SCORM model for developing game based learning contents in practice. In this work, we elicit some SCORM data elements that is useful for representing game run-time data, and apply those elements to SCORM sequencing of game based learning SCOs(Sharable Content Objects). We thus present the whole procedure of developing SCORM game based learning contents with a sample contents.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.77-84
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• 2019

Workflow management system is a system that manages the workflow model which defines the process of work in reality. We can define the workflow process by sequencing jobs which is performed by the performers. Using the workflow management system, we can also analyze the flow of the process and revise it more efficiently. Many researches are focused on how to make the workflow process model more efficiently and manage it more easily. Recently, many researches use the workflow log files which are the execution history of the workflow process model performed by the workflow management system. Ourresearch group has many interests in making useful knowledge from the workflow event logs. In this paper we use XES log files because there are many data using this format. This papersuggests what are the cardinalities of the temporal workcases and how to get them from the workflow event logs. Cardinalities of the temporal workcases are the occurrence pattern of critical elements in the workflow process. We discover instance cardinalities, activity cardinalities and organizational resource cardinalities from several XES-based workflow event logs and visualize them. The instance cardinality defines the occurrence of the workflow process instances, the activity cardinality defines the occurrence of the activities and the organizational cardinality defines the occurrence of the organizational resources. From them, we expect to get many useful knowledge such as a patterns of the control flow of the process, frequently executed events, frequently working performer and etc. In further, we even expect to predict the original process model by only using the workflow event logs.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.153-166
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• 2021

A main goal of pharmacogenomics studies is to predict individual's drug responsiveness based on high dimensional genetic variables. Due to a large number of variables, feature selection is required in order to reduce the number of variables. The selected features are used to construct a predictive model using machine learning algorithms. In the present study, we applied several hybrid feature selection methods such as combinations of logistic regression, ReliefF, TurF, random forest, and LASSO to a next generation sequencing data set of 400 epilepsy patients. We then applied the selected features to machine learning methods including random forest, gradient boosting, and support vector machine as well as a stacking ensemble method. Our results showed that the stacking model with a hybrid feature selection of random forest and ReliefF performs better than with other combinations of approaches. Based on a 5-fold cross validation partition, the mean test accuracy value of the best model was 0.727 and the mean test AUC value of the best model was 0.761. It also appeared that the stacking models outperform than single machine learning predictive models when using the same selected features.