• Genomics & Informatics
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• v.11 no.2
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• pp.68-75
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• 2013

Anaplastic thyroid cancer (ATC) belongs to the most malignant and rapidly progressive human thyroid cancers and its prognosis is very poor. Also, it shows high resistance to cancer treatments, so that effective treatment for ATC has not been found to date, and virtually all patients terminate their life rapidly after diagnosis. Although targeted treatment of genetic alterations has emerged as an extremely promising approach to human cancers, such as BRAF in metastatic melanoma, it remains unclear that how commonly genomic alterations are influenced in ATC tumorigenesis. In recent years, genome wide approaches have been exploited to find genetic alterations associated with complex diseases, including cancer. Here, we reviewed the comprehensive genetic alterations in ATC and recent approaches in the context of identifying genomic alterations associated with ATC. Since surprisingly few reports have been published on the genome wide study of ATC, this review puts emphasis on the urgent needs of genomic research for the prevention and treatment of ATC.

• Journal of the Korea Safety Management & Science
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• v.9 no.4
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• pp.129-136
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• 2007

This paper considers the sequencing of products in mixed model assembly lines under Just-In-Time (JIT) systems. Under JIT systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. The sequencing problem is solved using Genetic Algorithm Genetic Algorithm is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the Korea Society for Simulation
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• v.12 no.1
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• pp.59-71
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• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constraints. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model using a commercial genetic algorithm based optimizer. The results for computational experiments show that the real size problems we encountered can be solved in reasonable time.

• Journal of Electrical Engineering and information Science
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• v.2 no.5
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• pp.51-59
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• 1997

This paper presets a methodology of improving a conventional model in power systems using Genetic Algorithms(GAs) and suggests a GAs-based model which can directly solve the real-valued optimum in an optimization procedure. In applying GAs to the power flow, a new fitness mapping method is proposed using the proposed using the probability distribution function for all the payoffs in the population pool. In this approach, both the notions on a way of the genetic representation, and a realization of the genetic operators are fully discussed to evaluate he GAs' effectiveness. The proposed method is applied to IEEE 5-bus, 14-bus and 25-bus systems and, the results of computational experiments suggest a direct applicability of GAs to more complicated power system problems even if they contain nonlinear algebraic equations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1314-1323
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• 2011

The maintenance of generating units is implicitly related with power system reliability and has a tremendous bearing on the operation of the power system. A technique using a fuzzy search method which is based on fuzzy multi-criteria function has been proposed for GMS (generator maintenance scheduling) in order to consider multi-objective function. In this study, a new technique using combined fuzzy set theory and genetic algorithm(GA) is proposed for generator maintenance scheduling. The genetic algorithm(GA) is expected to make up for that fuzzy search method might search the local solution. The effectiveness of the proposed approach is demonstrated by the simulation results on a practical size test systems.

• ALGAE
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• v.34 no.1
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• pp.35-43
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• 2019

Genetic study of haploid organisms offers the advantage that mutant phenotypes are directly displayed, but has the disadvantage that strains carrying lethal mutations are not readily maintained. We describe an approach for generating and performing genetic analysis of diploid strains of Chlamydomonas reinhardtii, which is normally haploid. First protocol utilizes self-mating diploid strains that will facilitate the genetic analysis of recessive lethal mutations by offering a convenient way to produce homozygous diploids in a single mating. Second protocol is designed to reduce the chance of contamination and the accumulation of spontaneous mutations for long-term storage of mutant strains. Third protocol for inducing the meiotic program is also included to produce haploid mutant strains following tetraploid genetic analysis. We discuss implication of self-fertile strains for the future of Chlamydomonas research.

• Journal of applied mathematics & informatics
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• v.40 no.5_6
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• pp.933-947
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• 2022

In this research, we presented the type 2 fuzzy transportation problem with additional constraints and solved by our proposed genetic algorithm model, and the results are verified using the softwares, genetic algorithm tool in Matlab and Lingo. The goal of our approach is to minimize the cost in solving a transportation problem with an additional constraint (TPAC) using the genetic algorithm (GA) based type 2 fuzzy parameter. We reduced the type 2 fuzzy set (T₂FS) into a type 1 fuzzy set (T₁FS) using a critical value-based reduction method (CVRM). Also, we use the centroid method (CM) to obtain the corresponding crisp value for this reduced fuzzy set. To achieve the best solution, GA is applied to TPAC in type 2 fuzzy parameters. A real-life situation is considered to illustrate the method.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5353-5361
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• 2012

Interleukin-18 (IL-18) is an immune-stimulatory cytokine with antitumor activity in preclinical models. It plays pivotal roles in linking inflammatory immune responses and tumor progression and is a useful candidate in gene therapy of lymphoma or lymphoid leukemia. A phase I study of recombinant human IL-18 (rhIL-18) in patients with advanced cancer concluded that rhIL-18 can be safely given in biologically active doses to patients with advanced cancer. Some viruses can induce the secretion of IL-18 for immune evasion. The individual cytokine activity might be potentiated or inhibited by combinations of cytokines. Here we focus on combinational effects of cytokines with IL-18 in cancer progression. IL-18 is an important non-invasive marker suspected of contributing to metastasis. Serum IL-18 may a useful biological marker as independent prognostic factor of survival. In this review we cover roles of IL-18 in immune evasion, metastasis and angiogenesis, applications for chemotherapy and prognostic or diagnostic significance.

• The Plant Pathology Journal
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• v.35 no.6
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• pp.692-697
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• 2019

Agrobacterium rhizogenes-mediated transformation of sugar beet hairy roots expressing single-chain variable fragment (scFv) was exploited to evaluate the efficacy of four antibody-based constructs for interfering with the Beet necrotic yellow vein virus infection. The scFv specific to a major coat protein of virus, p21, was targeted to various cellular compartments including the cytosol (pIC and pICC constructs), apoplast (pIA), and mitochondrion (pIM). After mechanical virus inoculation, most of the hairy root clones expressing scFv in the cytosol displayed low virus titers while the majority of transgenic hairy root clones accumulated antibody in outer membrane of mitochondria or apoplast were infected. This hairy root system provided an efficient and rapid approach to initially investigating root disease resistance like rhizomania prior to transform whole recalcitrant plants such as sugar beet.

• BMB Reports
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• v.43 no.12
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• pp.836-841
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• 2010

In the era of personal genomics, predicting the individual response to drug-treatment is a challenge of biomedical research. The aim of this study was to validate whether interaction information between genetic and transcriptional signatures are promising features to predict a drug response. Because drug resistance/susceptibilities result from the complex associations of genetic and transcriptional activities, we predicted the inter-relationships between genetic and transcriptional signatures. With this concept, captured genetic polymorphisms and transcriptional profiles were prepared in cancer samples. By splitting ninety-nine samples into a trial set (n = 30) and a test set (n = 69), the outperformance of relationship-focused model (0.84 of area under the curve in trial set, P = $2.90{\times}10^{-4}$) was presented in the trial set and validated in the test set, respectively. The prediction results of modeling show that considering the relationships between genetic and transcriptional features is an effective approach to determine outcome predictions of drug-treatment.