• 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.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.589-598
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• 2019

Objective: To evaluate whether data acquired from perfusion computed tomography (PCT) parameters can aid in the prediction of treatment outcome after palliative chemotherapy in patients with unresectable advanced gastric cancer (AGC). Materials and Methods: Twenty-one patients with unresectable AGCs, who underwent both PCT and palliative chemotherapy, were prospectively included. Treatment response was assessed according to Response Evaluation Criteria in Solid Tumors version 1.1 (i.e., patients who achieved complete or partial response were classified as responders). The relationship between tumor response and PCT parameters was evaluated using the Mann-Whitney test and receiver operating characteristic analysis. One-year survival was estimated using the Kaplan-Meier method. Results: After chemotherapy, six patients exhibited partial response and were allocated to the responder group while the remaining 15 patients were allocated to the non-responder group. Permeability surface (PS) value was shown to be significantly different between the responder and non-responder groups (51.0 mL/100 g/min vs. 23.4 mL/100 g/min, respectively; p = 0.002), whereas other PCT parameters did not demonstrate a significant difference. The area under the curve for prediction in responders was 0.911 (p = 0.004) for PS value, with a sensitivity of 100% (6/6) and specificity of 80% (12/15) at a cut-off value of 29.7 mL/100 g/min. One-year survival in nine patients with PS value > 29.7 mL/100 g/min was 66.7%, which was significantly higher than that in the 12 patients (33.3%) with PS value ≤ 29.7 mL/100 g/min (p = 0.019). Conclusion: Perfusion parameter data acquired from PCT demonstrated predictive value for treatment outcome after palliative chemotherapy, reflected by the significantly higher PS value in the responder group compared with the non-responder group.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.141-158
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• 2019

Objectives: An adverse outcome pathway is a biological pathway that disturbs homeostasis and causes toxicity. It is a conceptual framework for organizing existing biological knowledge and consists of the molecular initiating event, key event, and adverse output. The AOP concept provides intuitive risk identification that can be helpful in evaluating the carcinogenicity of chemicals and in the prevention of cancer through the assessment of chemical carcinogenicity predictions. Methods: We reviewed various papers and books related to the application of AOPs for the prevention of occupational cancer. We mainly used the internet to search for the necessary research data and information, such as via Google scholar(http://scholar.google.com), ScienceDirect(www.sciencedirect.com), Scopus(www.scopus. com), NDSL(http: //www.ndsl.kr/index.do) and PubMed(http://www.ncbi.nlm.nih.gov/pubmed). The key terms searched were "adverse outcome pathway," "toxicology," "risk assessment," "human exposure," "worker," "nanoparticle," "applications," and "occupational safety and health," among others. Results: Since it focused on the current state of AOP for the prediction of toxicity from chemical exposure at work and prospects for industrial health in the context of the AOP concept, respiratory and nanomaterial hazard assessments. AOP provides an intuitive understanding of the toxicity of chemicals as a conceptual means, and it works toward accurately predicting chemical toxicity. The AOP technique has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment. AOP can be applied to the assessment of chemical carcinogenicity along with efforts to understand the effects of chronic toxic chemicals in workplaces. Based on these predictive tools, it could be possible to bring about a breakthrough in the prevention of occupational and environmental cancer. Conclusions: The AOP tool has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment and has been widely used in the field of chemical risk assessment and the evaluation of carcinogenicity at work. It will be a useful tool for prediction, and it is possible that it can help bring about a breakthrough in the prevention of occupational and environmental cancer.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.741-751
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• 2000

To achieve high performance by exploiting instruction level parallelism aggressively in superscalar processors, it is necessary to overcome the limitation imposed by control dependences and data dependences which prevent instructions from executing parallel. Value prediction is a technique that breaks data dependences by predicting the outcome of an instruction and executes speculatively its data dependent instruction based on the predicted outcome. In this paper, a hybrid value prediction scheme with dynamic classification mechanism is proposed. We design a hybrid predictor by combining the last predictor, a stride predictor and a two-level predictor. The choice of a predictor for each instruction is determined by a dynamic classification mechanism. This makes each predictor utilized more efficiently than the hybrid predictor without dynamic classification mechanism. To show performance improvements of our scheme, we simulate the SPECint95 benchmark set by using execution-driven simulator. The results show that our scheme effect reduce of 45% hardware cost and 16% prediction accuracy improvements comparing with the conventional hybrid prediction scheme and two-level value prediction scheme.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1445-1448
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• 2016

Purpose: The current study aimed at assessing the association between neutrophil-lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR) for the prognosis of the surgical outcome of epithelial ovarian cancer (EOC). Materials and Methods: EOC patient medical records of surgical operations between January, 2005 and December, 2015 were reviewed and their data of clinicopathological complete blood counts (CBCs) and surgical outcomes were collected. To assess their effects on surgical outcomes, PLR and NLR optimal predictive values were determined and then compared with each other. Results: A statistically significant relation was found between surgical outcomes and NLR and PLR (p<0.001 and p<0.001), for which new cutoff points were gained (PLR: 192,3,293; NLR: 3). The sensitivity and specificity were 0.74 and 0.67, respectively for PLR and 0.74 and 0.58, for NLR. Conclusions: NLR and PLR seem to be useful methods for the prediction of surgical outcomes in patients with EOCs. Increased NLR and PLR proved to be beneficial for poor surgical outcomes. Moreover, PLR increase showed further help in the predicting outcome of EOC suboptimal debulking.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Journal of Digestive Cancer Research
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• v.5 no.1
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• pp.5-15
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• 2017

Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related death in the world. The aggressive but not always predictable pattern of HCC causes the limited treatment option and poorer outcome. Many researches had already proven the heterogeneity of HCC is one of the major challenges for treatment option and prognosis prediction. Molecular subtyping of HCC and selection of patient based on molecular profile can provide the optimization in the treatment and prognosis prediction. In this review, we have tried to summarize the molecular classification of HCC proposed by different valuable researches presented in the logistic way.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.240-245
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• 2018

Purpose: Early prediction of prognosis of children resuscitated from cardiac arrest is a major challenge. We investigated the utility of electroencephalography (EEG) and laboratory studies for predicting of neurologic outcome in children resuscitated from cardiac arrest. Methods: We retrospectively analyzed medical records of patients who were resuscitated from cardiac arrest from 2006 to 2015 at the Gil Medical Center. Patients aged one month to 18 years were included. EEG analysis included background scoring, reactivity and seizure burden. EEG background was classified score 0 (normal/organized), score 1 (slow and disorganized), score 2 (discontinuous or burst suppression), and score 3 (suppressed and featureless). Neurologic outcome was evaluated by Pediatric Cerebral Performance Category (PCPC) at least 6 months after cardiac arrest. Results: Total 26 patients were evaluated. Nine patients showed good neurologic outcome (PCPC 1, 2, 3) and 17 patients showed poor neurologic outcome (PCPC 4, 5, 6). Patients of poor neurologic outcome group showed EEG background score 3 in 88.2%, whereas 44.4% in patients of good neurologic outcome group (P=0.028). Electrographic ictal discharges except non-convulsive status epilepticus were presented in 44.4% of good neurologic outcome group and 5.9% of poor neurologic outcome group (P=0.034). Ammonia and lactate levels were higher and pH levels were lower in poor outcome group than good neurologic outcome group. Conclusion: Suppressed and featureless EEG background is associated with poor neurologic outcome and electrographic seizures are associated with good neurologic outcome.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4143-4145
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• 2014

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) Ewing sarcoma (ES) outcome data. The aim of this study was to identify and optimize ES-specific survival prediction models and sources of survival disparities. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ES. 1844 patients diagnosed between 1973-2009 were used for this study. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome (bone and joint specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: The mean follow up time (S.D.) was 74.48 (89.66) months. 36% of the patients were female. The mean (S.D.) age was 18.7 (12) years. The SEER staging has the highest ROC (S.D.) area of 0.616 (0.032) among the factors tested. We simplified the 4-layered risk levels (local, regional, distant, un-staged) to a simpler non-metastatic (I and II) versus metastatic (III) versus un-staged model. The ROC area (S.D.) of the 3-tiered model was 0.612 (0.008). Several other biologic factors were also predictive of ES-specific survival, but not the socio-economic factors tested here. Conclusions: ROC analysis measured and optimized the performance of ES survival prediction models. Optimized models will provide a more efficient way to stratify patients for clinical trials.

• Korean Journal of Radiology
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• v.2 no.2
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• pp.63-67
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• 2001

Objective: Atrophy and a high T2 signal of the hippocampus are known to be the principal MR imaging findings of hippocampal sclerosis. The purpose of this study was to determine whether or not individual MRI findings correlate with surgical outcome in patients with this condition. Materials and Methods: Preoperative MR imaging findings in 57 consecutive patients with pathologically-proven hippocampal sclerosis who underwent anterior temporal lobectomy and were followed-up for 24 months or more were retrospectively reviewed, and the results were compared with the postsurgical outcome (Engel classification). The MR images included routine sagittal T1-weighted and axial T2-weighted spin-echo images, and oblique coronal T1-weighted 3D gradient-echo and T2-weighted 2D fast spin-echo images obtained on either a 1.5 T or 1.0 T unit. The images were visually evaluated by two neuroradiologists blinded to the outcome; their focus was the presence or absence of atrophy and a high T2 hippocampal signal. Results: Hippocampal atrophy was seen in 96% of cases (55/57) [100% (53/53) of the good outcome group (Engel class I and II), and 50% (2/4) of the poor outcome group (class III and IV)]. A high T2 hippocampal signal was seen in 61% of cases (35/57) [62% (33/53) of the good outcome group and 50% (2/4) of the poor outcome group]. All 35 patients with a high T2 signal had hippocampal atrophy. 'Normal' hippocampus, as revealed by MR imaging, occurred in 4% of patients (2/57), both of whom showed a poor outcome (Engel class III). The presence or absence of hippocampal atrophy correlated well with surgical outcome (p<0.01). High T2 signal intensity did not, however, significantly correlate with surgical outcome (p>0.05). Conclusion: Compared with a high T2 hippocampal signal, hippocampal atrophy is more common and correlates better with surgical outcome. For the prediction of this, it thus appears to be the more useful indicator.