• Asian Pacific Journal of Cancer Prevention
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• 제17권1호
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• pp.95-100
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• 2016

Background: Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high-risk and low-risk groups. Materials and Methods: In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. Results: In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). Conclusions: The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

• 한국환경과학회지
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• 제5권3호
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• pp.337-346
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• 1996

The purpose of this study was to provide basic data of the growth characteristics of the landscape trees for better landscape planting design, construction and maintenance through the prediction of landscape change as time passes fly the analysis of survival rate, distribution patterns & increment percent of tree height, width, stem diameter (breast or surface) of widely used six tree species in Seongnam-si Eunhang-jugong apartment housing areas (8 years have passed after landsape alanting work). The main results can be summarized as followed. The tree survival rate of Pinus parviflora was the highest rate 89.2% than any other species, but Acer buergerianum showed the lowest survival rate at that of it 35.0%, & that of Picea abies 70.5 %, Metasequoia glyptostroboides 71.6%, Maknolia denudata 38.9%, Acer paimatum was 71.7%, As a whole, the tree survival rate of coniferous trees were relatively high. The tree height increment percent of the deciduous species wert relatively high. And that of Metasequoia glyptostroboides was the highest rate 11.61% than any other species, but that of Magnolia denudata was the lowest rate 5.59% than any other species. According to this results, the increment percent of trees in this apartment areas were comparatively lower than that of each related species planted in nursery area. And this results would be considered when landscape experts do landscape planting design, construction & maintenance. The distribution patterns of present tree size showed a Normal Distribution like any other biological features.

• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.203-209
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• 2009

Frailty models are useful for the analysis of correlated and/or heterogeneous survival data. However, the inferences of fixed parameters, rather than random effects, have been mainly studied. The prediction (or estimation) of random effects is also practically useful to investigate the heterogeneity of the hospital or patient effects. In this paper we propose how to extend the prediction method for random effects in HGLMs (hierarchical generalized linear models) to log-normal semiparametric frailty models with nonparametric baseline hazard. The proposed method is demonstrated by a simulation study.

• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1439-1448
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• 2022

Background: We investigated the feasibility of in vitro radiosensitivity prediction with gene expression using deep learning. Methods: A microarray gene expression of the National Cancer Institute-60 (NCI-60) panel was acquired from the Gene Expression Omnibus. The clonogenic surviving fractions at an absorbed dose of 2 Gy (SF2) from previous publications were used to measure in vitro radiosensitivity. The radiosensitivity prediction model was based on the convolutional neural network. The 6-fold cross-validation (CV) was applied to train and validate the model. Then, the leave-one-out cross-validation (LOOCV) was applied by using the large-errored samples as a validation set, to determine whether the error was from the high bias of the folded CV. The criteria for correct prediction were defined as an absolute error<0.01 or a relative error<10%. Results: Of the 174 triplicated samples of NCI-60, 171 samples were correctly predicted with the folded CV. Through an additional LOOCV, one more sample was correctly predicted, representing a prediction accuracy of 98.85% (172 out of 174 samples). The average relative error and absolute errors of 172 correctly predicted samples were 1.351±1.875% and 0.00596±0.00638, respectively. Conclusion: We demonstrated the feasibility of a deep learning-based in vitro radiosensitivity prediction using gene expression.

• 한국국방경영분석학회지
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• 제17권1호
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• pp.159-176
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• 1991

The probability of target survival is the most important factor in the target assignment, Most of the studies about it have assumed the case of one target and ane weapon type. Therefore, they can not be applied to the real situation. In this paper. the quantity and type of enemy assets of the friendly force are considered simultaneously. Considered defense type is the coordinated defense with no impact point prediction. The objective function is to minimize the expected total survival value of targets which are scattered in the defense area. The rules of aircraft assignment are as follows : first, classify targets into several groups, each of those has the same desired damage level secondly. select the critical group which has the least survival value in accordance with the additional aircraft assignment, and finally. assign the same number of attack assets against each target in the critical group. In this paper, the attack assets, the escort assets, and the defense assets are considered. The model is useful to not only the simple aircraft assignment problem but also the complicated wargame models.

• 디지털산업정보학회논문지
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• 제17권1호
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• pp.109-115
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• 2021

The number of freshmen at universities is decreasing due to the recent decline in the school-age population, and the survival of many universities is threatened. To overcome this situation, universities are seeking ways to use big data within the school to improve the quality of education. A study on the prediction of dropout students is a representative case of using big data in universities. The dropout prediction can prepare a systematic management plan by identifying students who will drop out of school due to reasons such as dropout or expulsion. In the case of actual on-campus data, a large number of missing values are included because it is collected and managed by various departments. For this reason, it is necessary to construct a model by effectively reflecting the missing values. In this study, we propose a university student dropout prediction model based on eXtreme Gradient Boost that can be applied to data with many missing values and shows high performance. In order to examine the practical applicability of the proposed model, an experiment was performed using data from C University in Chungbuk. As a result of the experiment, the prediction performance of the proposed model was found to be excellent. The management strategy of dropout students can be established through the prediction results of the model proposed in this paper.

• Asian Pacific Journal of Cancer Prevention
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• 제13권11호
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• pp.5715-5719
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• 2012

The relation between cyclooxygenase enzymes and E-cadherin, along with the roles of these markers in the prediction of survival in optimally cytoreduced serous ovarian cancer patients was investigated. Individuals who underwent primary staging surgery and achieved optimal cytoreduction (largest residual tumor volume <1 cm) constituted the study population. Specimens of 32 cases were immunohistochemically examined for cyclooxygenase-1, cyclooxygenase-2, and E-cadherin. Two could not be evaluated for E-cadherin and cyclooxygenase-1. Overall, 14/30, 19/30, and 15/32 cases were positive for E-cadherin, cyclooxygenase-1, and cyclooxygenase-2, respectively. The expressions of E-cadherin and cyclooxygenase-2 were inversely correlated (p:0.02). E-cadherin expression was related with favorable survival (p<0.001). The relation between the expression of cyclooxygenase enzymes and poor survival did not reach statistical significance. On multivariate analysis, E-cadherin appeared as an independent prognostic factor for survival. In conclusion, E-cadherin expression is strongly linked with favorable survival. E-cadherin and cyclooxygenase 2 may interact with each other during the carcinogenesis-invasion process. Further studies clarifying the relation between E-cadherin and cyclooxygenase enzymes may lead to new preventive and therapeutic targets in ovarian cancer.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5081-5084
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• 2015

Background: Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. Materials and Methods: A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. Results: The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. Conclusions: Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.

• Asian Pacific Journal of Cancer Prevention
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• 제14권5호
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• pp.3331-3335
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• 2013

Background: The incidence of prostate cancer, one of the most common cancers in elderly men, is increasing annually in Thailand. Matrix metalloproteinase 11 (MMP-11) is a member of the extracellular matrix metalloproteases which has been associated with human tumor progression and clinical outcome. Aim: To quantify MMP-11 expression in prostatic adenocarcinoma tissues and to determine whether its overexpression correlates with survival outcome, and to assess its potential as a new prognostic marker. Materials and Methods: Expression of MMP-11 was analyzed using immunohistochemistry in 103 Thai patients with prostatic adenocarcinoma. Overall survival was analyzed using Kaplan-Meier methods and Cox regression models. Results: Immunoreactivity of MMP-11 was seen in the stroma of prostatic adenocarcinoma tissue samples, high expression being significantly correlated with poor differentiation in Gleason grading, pathologic tumor stage 4 (pT4), and positive-bone metastasis (p<0.05), but not age and prostatic-specific antigen (PSA) level. Patients with high levels of MMP-11 expression demonstrated significantly shorter survival (p<0.001) when compared to those with low levels. Multivariate analysis showed that MMP-11 expression and pT stage were related with survival in prostatic adenocarcinoma [hazard ratio (HR)=0.448, 95% confidence interval (95%CI)=0.212-0.946, HR=0.333, 95%CI=0.15-0.74, respectively]. Conclusions: Expression of MMP-11 is significantly associated with survival in prostatic adenocarcinoma. High levels may potentially be used for prediction of a poor prognosis.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1189-1198
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• 2015

글로벌 금융 위기 발생으로 헤지펀드의 영향력이 증가하면서 헤지펀드의 위험도와 생존여부를 가늠할 새로운 접근법이 필요하게 되었다. 본 연구에서는 헤지펀드의 데이터를 입력값으로 하는 퍼지신경망 모형을 통해 헤지펀드의 생존여부를 예측한다. 헤지펀드의 데이터는 그 변수가 불명확하고 내재적인 불확실성을 가지고 있어 생존 여부의 경계를 설정하는데 어려움이 있다. 따라서 생존 여부를 소속정도로 평가하여 불확실성을 모사할 수 있는 퍼지신경망 모형을 적용하여 예측하고 정확도를 평가한다. 또한 다른 인공지능 방법론들을 이용하여 평가한 결과와 제시한 모형의 성과를 비교하여 그 차이점을 확인한다. 본 연구의 실험결과를 통해 퍼지신경망 모형의 예측력을 확인했으며, 향후 투자자들이 헤지펀드 투자에 대한 의사를 결정하는데 도움을 줄 것으로 기대한다.