• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.387-395
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• 2010

It is important to identify the interaction of genes about human disease and characteristic value. Many studies as like logistic analysis, have associated being pursued, but, previous methods did not consider the sub-group of the genotypes. So, QTL interactions and the GMM (genotype matrix mapping) have been developed. In this study, we detect the superior genotype combination to have an impact on economic traits of Korean cattle based on the study over GMM method. Thus, we identified interaction effects of single nucleotide polymorphisms (SNPs) responsible for average daily gain(ADG), marbling score (MS), carcass cold weight (CWT), longissimus muscle dorsiarea (LMA) using GMM method. In addition, we examine significance of the major genotype combination selected by implementing permutation test of the F-measure which was not obtained by Sachiko et al.

• Journal of Oral Medicine and Pain
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• v.44 no.4
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• pp.160-168
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• 2019

Purpose: To search the salivary factors that objectively indicate an pain in myalgia patients with temporomandibular joint disorder (TMD) and determine the possibility of the factors as pain-biomarkers. Methods: Participants consisted of pain-free 15 persons (male 7, female 8, mean age±standard deviation (SD); 26.8±16.04 years) and 45 myalgia patients with TMD (male 21, female 24, mean age±SD; 27.98±13.01 years). They were divided into a pain-free group (numerical rating scale [NRS] score 0), a mild pain group (NRS 1-4), a moderate pain group (NRS 5-6), and a severe pain group (NRS 7-10) and members of all groups were age, sex matched. Interleukin-8 (IL-8) and matrix metalloprotease 9 (MMP-9) were selected as pain biomarkers, by searching the Gene Expression Omnibus database and analyzing pain-related genes. Enzyme-linked immunosorbent assays were used to measure the concentration of IL-8 and MMP-9 in the patients' saliva. Results: IL-8 and MMP-9 levels were statistically significantly higher in pain groups than in the pain-free group. Greater differences were observed in patients with acute pain (with painful duration under 3 months) than in the control group and in female patients than in male. Conclusions: Salivary IL-8 and MMP-9 may play a role as biomarkers of myalgia in patients with TMD.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.173-178
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• 2022

Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

• School Mathematics
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• v.10 no.2
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• pp.259-277
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• 2008

Conventional assessments only provide a single summary score that indicates the overall performance level or achievement level of a student in a single learning area. For assessments to be more effective, test should provide useful diagnostic information in addition to single overall scores. Cognitive diagnosis modeling provides useful information by estimating individual knowledge states by assessing whether an examinee has mastered specific attributes measured by the test(Embretson, 1990; DiBello, Stout, & Rousses, 1995; Tatsuoka, 1995). Attributes are skills or cognitive processes that are required to perform correctly on a particular item. By the results of this study, students, parents, and teachers would be able to see where a student stands with respect to mastering the attributes. Such information could be used to guide the learner and teacher toward areas requiring more study. By being able to assess where they stand in regard to the attributes that compose an item, students can plan a more effective learning path to be desired proficiency levels. It would be very helpful to the examinee if score reports can provide the scale scores as well as the skill profiles. While the scale scores are believed to provide students' math ability by reporting only one score point, the skill profiles can offer a skill level of strong, weak or mixed for each student for each skill.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.291-300
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• 2019

This study investigates the characteristics of evaluation measures for classification models on a binary response variable in order to evaluate their suitability for use. Six measures are considered: Accuracy, Sensitivity, Specificity, Precision, F-measure, and the Heidke's skill score (HSS). Evaluation measures are reformulated using x(ratio of actually 1), y(ratio predicted by 1), z(ratio of both actual and predicted by 1) from the confusion matrix. We suggest two necessary conditions to assess the suitability of the evaluation measures. The first condition is that the measure function is constant for x and y in the case of a random model. The second condition is that the measure function is increasing for z and decreasing for x and y. Since only HSS satisfies the two conditions, that is always appropriate as an evaluation measure for the classification model on the binary response variable, and the other measures should be used within a limited range.

• Journal of Korean Neurosurgical Society
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• v.65 no.6
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• pp.825-833
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• 2022

Objective : ABM/P-15 (anorganic bone matrix/15-amino acid peptide fragment) is a commercially available synthetically manufactured P-15 collagen peptide fragment, that is adsorbed on ABM. This study was done to investigate the efficacy of ABM/P-15 in achieving fusion in the lumbar spine and comparing it with that of recombinant bone morphogenic protein-2 (rhBMP-2) and demineralized bone matrix (DBM). Methods : A retrospective observational study of prospectively collected data of 140 patients who underwent lumbar spinal fusion surgeries in a single specialty spine hospital between 2016 and 2020, with a minimum 6-month follow-up was conducted. Based on the material used for the augmentation of the bone graft at the fusion site, the patients were divided into three categories namely ABM/P-15, rhBMP-2, and DBM group. Results : ABM/P-15, rhBMP-2, and DBM were used in 46, 44, and 50 patients, respectively. Patient characteristics like age, gender, bone mineral density, smoking history, and presence of diabetes mellitus were comparable amongst the three groups. Average follow-up was 16.0±5.2, 17.9±9.8, and 26.2±14.9 months, respectively in ABM/P-15, rhBMP-2, and DBM groups. The fusion was achieved in 97.9%, 93.2%, and 98% patients while the average time-to-union was 4.05±2.01, 10±4.28, and 9.44±3.49 months (p<0.001), respectively for ABM/P-15, rhBMP-2, and DBM groups. The average pre-operative Visual analogue scale score was 6.93±2.42, 7.14±1.97, 7.01±2.14 (p=0.900) for ABM/P-15, rhBMP-2 and DBM groups, respectively, which reduced to 1.02±0.80, 1.21±0.96, and 0.54±0.70 (p=0.112), respectively at the last follow up. Pre-operative Oswestry disability index scores were 52.7±18.02, 55.4±16.8, and 53.56±19.6 (p=0.751) in ABM/P-15, rhBMP-2, and DBM groups, which post-operatively reduced to 33.77±15.52, 39.42±16.47, and 38.3±15.89 (p=0.412) and further to 15.74±8.3, 17.41±10.45, and 16.76±9.81 (p=0.603), respectively at the last follow-up. Conclusion : ABM/P-15 appears to achieve union significantly earlier than rhBMP-2 and DBM in lumbar spinal fusion cases while maintaining a comparable clinical and complication profile.

• Korean Journal of Environment and Ecology
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• v.33 no.6
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• pp.624-635
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• 2019

Bird strike accidents, a collision between aircraft and birds, have been increasing annually due to an increasing number of aircraft operating each year to meet heavier demand for air traffic. As such, many airports have conducted studies to assess and manage bird strike risks effectively by identifying and ranking bird species that can damage aircraft based on the bird strike records. This study was intended to investigate the bird species that were likely to threaten aircraft and compare and discuss the risk of each species estimated by the single-matrix and multi-matrix risk assessment models based on the Integrated Flight Information Service (IFIS) data collected in Gimpo, Gimhae and Jeju Airports in South Korea from 2005 to 2013. We found that there was a difference in the assessment results between the two models. The single-matrix model estimated 2 species and 6 taxa in Gimpo and Gimhae Airports and 2 species and 5 taxa in Jeju Airport to have the risk score above "high," whereas the multi-matrix model estimated 3 species and 5 taxa in Gimpo Airport, 4 species and 5 taxa in Gimhae Airport, and 2 species and 3 taxa in Jeju Airport to have the risk score above "very high." Although both models estimated the similar high-risk species in Gimpo and Gimhae Airports, there was a significant difference in Jeju Airport. Gimpo and Gimhae Airports are near the estuary of a river, which is an excellent habitat for large and heavy waterbirds. On the other hand, Jeju Airport is near the coast and the city center, and small and light bird species are mostly observed. Since collisions with such species have little effect on aircraft fuselage, the impact of common variables between the two models was small, and the additional variables caused a significant difference between the estimation by the two models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.427-430
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• 2006

Clustering for gene expression data without filtering out noise genes may be distorted or derived inappropriate inference. Identifying significant genes and deleting noise before major analysis is necessary fur meaningful discovery from genes expression pattern. We proposed a new method of finding significant genes using factor analysis which is done on transposed data matrix. We construct significance score that is sum of factor loadings for declared significant number of factor, and set threshold through replication. Our proposed method works well for simulated time-course data for finding significant genes even though variance level gets larger.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.279-290
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• 2010

Technical countermeasures against debris flow should be established upon the risk level of the target location. Risk of debris flow should consider the hazard imposed by debris flow and vulnerability of the facilities to debris flow. In this research, we have defined the target location for risk evaluation and suggested scoring method of hazard of debris flow and vulnerability of road to debris flow. By defining risk rank into 6 categories in terms of possibility of damage during rainfall and using the risk scores of 46 debris flow cases, we have suggested risk ranking matrix. The method can be used in ranking the drainage basin adjacent to road by simply determining the hazard with vulnerability score and can be used for planning the debris flow countermeasures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.