• International journal of advanced smart convergence
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• v.9 no.1
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• pp.90-97
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• 2020

Oncolytic viruses are characterized by their ability to selectively kill cancer cells, and thus they have potential for application as novel anticancer agents. Despite an increase in the number of studies on methodologies involving oncolytic viruses, bioinformatic studies generating useful data are lacking. We constructed a database for oncolytic virus research (the oncolytic virus database, OVDB) by integrating scattered genetic information on oncolytic viruses and proposed a systematic means of using the biological data in the database. Our database provides data on 14 oncolytic viral strains and other types of viruses for comparative analysis. We constructed the OVDB using the basic local alignment search tool, and therefore can provides genetic information on highly homologous oncolytic viruses. This study contributes to facilitate systematic bioinformatics research, providing valuable data for development of oncolytic virus-based anticancer therapies.

• Journal of Ecology and Environment
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• v.45 no.3
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• pp.97-104
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• 2021

Background: Asarum sieboldii Miq., a species of forest understory vegetation, is an herbaceous perennial belonging to the family Aristolochiaceae. The metapopulation of A. sieboldii is distributed sparsely and has a short seed dispersal distance by ants as their seed distributor. It is known that many flowers of A. sieboldii depend on self-fertilization. Because these characteristics can affect negatively in genetic structure, investigating habitat structure and assessment of genetic structure is needed. A total of 27 individuals in a valley were sampled for measuring genetic diversity, genetic distance, and genetic differentiation by RAPDPCR. Results: The habitat areas of A. sieboldii metapopulation were relatively small (3.78~33.60 m²) and population density was very low (five to seven individuals in 20×20 m quadrat). The habitat of A. sieboldii was a very shady (relative light intensity = 0.9%) and mature forest with a high evenness value (J = 0.81~0.99) and a low dominance value (D = 0.19~0.28). The total genetic diversity of A. sieboldii was quite high (h = 0.338, I = 0.506). A total of 33 band loci were observed in five selected primers, and 31 band loci (94%) were polymorphic. However, genetic differentiation along the valley was highly progressed (G_st = 0.548, N_m = 0.412). The average genetic distance between subpopulations was 0.387. The results of AMOVA showed 52.77% of variance occurs among populations, which is evidence of population structuring. Conclusions: It is expected that a small-scale founder effect had occurred, an individual spread far from the original subpopulation formed a new subpopulation. However, geographical distance between individuals would have been far and genetic flow occurred only within each subpopulation because of the low density of population. This made significant genetic distance between the original and new population by distance. Although genetic diversity of A. sieboldii metapopulation is not as low as concerned, the subpopulation of A. sieboldii can disappear by stochastic events due to small subpopulation size and low density of population. To prevent genetic isolation and to enhance the stable population size, conservative efforts such as increasing the size of each subpopulation or the connection between subpopulations are needed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.147-152
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• 2000

In this paper we propose a Genetic Algorithm for the path planning of an autonomous mobile robot. Genetic Algorithms(GAs) have advantages of the adaptivity such as GAs work even if an environment is time-varying or unknown. Therefore, we propose the path planning algorithms using the GAs-based approach and show more adaptive and optimal performance by simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.279-282
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• 1996

Collision avoidance is a method to direct a mobile robot without collision when traversing the environment. This kind of navigation is to reach a destination without getting lost. In this paper, we use a genetic algorithm for the path planning and collision avoidance. Genetic algorithm searches for path in the entire, continuous free space and unifies global path planning and local path planning. It is a efficient and effective method when compared with traditional collision avoidance algorithm.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.22-26
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• 2003

This paper presents a genetic algorithm based autonomic movement algorithm for an autonomous underwater vehicle(AUV) and verified it to simulation. Also, developed program that can do simulation on two dimension and three dimension in seabed environment. The presented algorithm is applicable to a escape from the recursive search and a development of obstacle avoidance system.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2953-2955
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• 1999

In this paper, the concept of evolvable hardware and evolutionary robotics are introduced and constructing the mobile robot controller without human operator is suggested. The robot controller is evolved to avoid obstacles by genetic learning which determines the weights between sensor inputs and motor outputs. Genetic algorithms which is executed in a computer(PC) searches the best weights by interacting with robot performance under it's environment. The experiment is done by real mobile robot Khepera and a simple GA.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.81-81
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• 2017

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.83-83
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• 2017

• 한국약용작물학회:학술대회논문집
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• 2008.05a
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• pp.298-299
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• 2008

• Proceedings of the Korean Society of Environment and Ecology Conference
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• 2003.04a
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• pp.27-31
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• 2003