• Journal of KIISE:Databases
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• v.36 no.4
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• pp.247-253
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• 2009

Rank learning and processing have gained much attention in the IR and data mining communities for the last decade. While other data mining techniques such as classification and regression have been actively researched to interoperate with RDBMS by using the tightly coupled or loose coupling approaches, ranking has been researched independently without integrating into RDBMS. This paper proposes a tightly coupled integration of the Ranking SVM into MySQL in order to perform the rank learning task efficiently within the RDBMS. We implemented new SQL commands for learning ranking functions and predicting ranking scores. We evaluated our tightly coupled integration of Ranking SVM by comparing it to a loose coupling implementation. The experiment results show that our approach has a performance improvement of $10{\sim}40%$ in the training phase and 60% in the prediction phase.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3085-3091
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• 2013

Associations between ABCB1 and XPC genetic polymorphisms and risk of developing colorectal cancer (CRC) as well as clinical outcomes in CRCs with chemotherapy were investigated. A case-control study was performed on the ABCB1 C3435T, G2677T/A and XPC Lys939Gln polymorphisms in 428 CRC cases and 450 hospitalbased, age and sex frequency-matched controls using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assays. We observed that the ABCB1 3435CT or CC+CT variants were significantly linked with increasing risk of developing CRC (adjusted OR (95% CI): 1.814 (1.237-2.660), P=0.0022; adjusted OR (95% CI): 1.605 (1.117-2.306), P=0.0102, respectively). Moreover, the distribution frequency of XPC AC genotype or AC+CC genotypes also showed a tendency towards increasing the suscepbility for CRC (P=0.0759 and P=0.0903, respectively). Kaplan-Meier curves showed that the ABCB1 C3435T variant was associated with a tendency toward longer progression-free survival (PFS) (n=343, Log-rank test: P=0.063), and the G2677T/A variant genotypes (GT+TT+GA+AA) with a tendency for longer OS in postoperative oxaliplatin-based patients (n=343, Log-rank test: P=0.082). However, no correlation of the XPC Lys939Gln polymorphism was found with PFS and OS in patients with postoperative oxaliplatin-based chemotherapy (n=343). Our study indicated that ABCB1 polymorphisms might be candidate pharmacogenomic factors for the prediction of CRC susceptibility, but not for prognosis with oxaliplatin chemosensitivity in CRC patients.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.12
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• pp.1356-1363
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• 2009

The aim of this research is to construct eigenfrequency optimization codes for plates with Arbitrary Rank Microstructures. From among noise factors, resonance sound is main reason for floor's solid noise. But, Resonance-elusion design codes are not fixed so far. Besides, The prediction of composite material's capability and an resonance elusion by controlling natural frequency of plate depend on designer's experiences. In this paper, First, using computer program with arbitrary rank microstructure, variation on composite material properties is studied, and then natural frequency control is performed by plate topology optimization method. The results of this study are as followed. 1) Programs that calculate material properties along it's microstructure composition and control natural frequency on composite material plate are coded by Homogenization and Topology Optimization method. and it is examined by example problem. 2) Equivalent material properties, calculated by program, are examined for natural frequency. In this paper, Suggested programs are coded using $Matlab^{TM}$, Feapmax and Feap Library with Homogenization and Topology Optimization method. and Adequacy of them is reviewed by performing the maximization or minimization of natural frequency for plates with isotropic or anisotropic materials. Since the programs has been designed for widely use. If the mechanism between composite material and other structural member is identified, extension application may be possible in field of structure maintenance, reinforcement etc. through application of composite material.

• Journal of Soil and Groundwater Environment
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• v.13 no.4
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• pp.40-53
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• 2008

Groundwater pollution prediction methods have been developed to plan the sustainable groundwater usage and protection from potential pollution in many countries. DRASTIC established by US EPA is the most widely used groundwater vulnerability mapping method. However, the DRASTIC showed limitation in predicting the groundwater contamination because the DRASTIC method is designed to embrace only hydrogeologic factors. Therefore, in this study, three different methods were applied to improve a groundwater pollution prediction method: US EPA DRASTIC, Modified-DRASTIC suggested by Panagopoulos et al. (2006), and LSDG (Land use, Soil drainage, Depth to water, Geology) proposed by Rupert (1999). The Modified-DRASTIC is the modified version of the DRASTIC in terms of the rating scales and the weighting coefficients. The rating scales of each factor were calculated by the statistical comparison of nitrate concentrations in each class using the Wilcoxon rank-sum test; while the weighting coefficients were modified by the statistical correlation of each parameter to nitrate concentrations using the Spearman's rho test. The LSDG is a simple rating method using four factors such as Land use, Soil drainage, Depth to water, and Geology. Classes in each factor are compared by the Wilcoxon rank-sum test which gives a different rating to each class if the nitrate concentration in the class is significantly different. A database of nitrate concentrations in groundwaters from 149 wells was built in Keumsan area. Application of three different methods for assessing the groundwater pollution potential resulted that the prediction which was represented by a correlation (r) between each index and nitrate was improved from the EPA DRASTIC (r = 0.058) to the modified rating (r = 0.245), to the modified rating and weights (r = 0.400), and to the LSDG (r = 0.415), respectively. The LSDG seemed appropriate to predict the groundwater pollution in that it contained land use as a factor of the groundwater pollution sources and the rating of each class was defined by a real pollution nitrate concentration.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.165-175
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• 2005

Geographic Information System(GIS) is useful to preserve cultural heritage and land use management using both spatial information management technique and spatial analysis function in cultural heritage management. The purpose of this study is to build a database of pottery and kiln sites in South Korea, to analyze site locations and finally to make prediction model. The locations of 1,200 sites are put into GIS database. Such factor elevation, slope angle, aspect, horizontal/vertical distance from the nearest water are analyzed. Each factor was statistically analyzed on GIS and represented to rank 1-5. Pottery/kiln can be predicted by the spatial analysis function in overlay methods. As a result of this study, preliminary application of prediction model shows that the high potential area is between the slope and alluvial plain. Field survey in the Sungbuk-dong in Daejeon city supports the preliminary result. More data can make improve efficient prediction model in unknown areas.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1601-1607
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• 2013

A determination of circulating tumor cell (CTC) effectiveness for prediction of progression-free survival (PFS) and overall survival (OS) was conducted as an adjunct to standard treatment of care in breast cancer management. Between November 2008 and March 2009, 22 metastatic and 12 early stage breast carcinoma patients, admitted to Ankara Oncology Training and Research Hospital, were included in this prospective trial. Patients' characteristics, treatment schedules and survival data were evaluated. CTC was detected twice by CellSearch method before and 9-12 weeks after the initiation of chemotherapy. A cut-off value equal or greater than 5 cells per 7.5 ml blood sample was considered positive. All patients were female. Median ages were 48.0 (range: 29-65) and 52.5 (range: 35-66) in early stage and metastatic subgroups, respectively. CTC was positive in 3 (13.6%) patients before chemotherapy and 6 (27.3%) patients during chemotherapy in the metastatic subgroup whereas positive in only one patient in the early stage subgroup before and during chemotherapy. The median follow-up was 22.0 (range: 21-23) and 19.0 (range: 5-23) months in the early stage and metastatic groups, respectively. In the metastatic group, both median PFS and OS were significantly shorter in any time CTC positive patients compared to CTC negative patients (PFS: 4.0 vs 14.0 months, Log-Rank p=0.013; and OS: 8.0 months vs. 20.5 months, Log-Rank p<0.001). OS was affected from multiple visceral metastatic sites (p=0.055) and higher grade (p=0.044) besides CTC positivity (log rank p<0.001). Radiological response of chemotherapy was also correlated with better survival (p<0.001). As a result, CTC positivity was confirmed as a prospective marker even in a small patient population, in this single center study. Measurement of CTC by CellSearch method in metastatic breast carcinoma cases may allow indications of early risk of relapse or death with even as few as two measurements during a chemotherapy program, but this finding should be confirmed with prospective trials in larger study populations.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.767-776
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• 2008

Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.

• Atmosphere
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• v.17 no.3
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• pp.217-230
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• 2007

Using the Lorenz-95 simple model, which can simulate many atmospheric characteristics, we compare the performance of ensemble strategies such as bred vectors, the bred vectors rotated (to be orthogonal to each bred member), and the Ensemble Transform Kalman Filter (ETKF). The performance metrics used are the RMSE of ensemble means, the ratio of RMS error of ensemble mean to the spread of ensemble, rank histograms to see if the ensemble member can well represent the true probability density function (pdf), and the distribution of eigen-values of the forecast ensemble, which can provide useful information on the independence of each member. In the meantime, the orthogonal bred vectors can achieve the considerable progress comparing the bred vectors in all aspects of RMSE, spread, and independence of members. When we rotate the bred vectors for orthogonalization, the improvement rate for the spread of ensemble is almost as double as that for RMS error of ensemble mean compared to the non-rotated bred vectors on a simple model. It appears that the result is consistent with the tentative test on the operational model in KMA. In conclusion, ETKF is superior to the other two methods in all terms of the assesment ways we used when it comes to ensemble prediction. But we cannot decide which perturbation strategy is better in aspect of the structure of the background error covariance. It appears that further studies on the best perturbation way for hybrid variational data assimilation to consider an error-of-the-day(EOTD) should be needed.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.185-190
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• 2014

Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for high-lonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Loneliness-associated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.