Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms

Ke, Wan-Sheng; Hwang, Yuchi; Lin, Eugene

doi:10.2147/aabc.s8656

Cited by 8 publications

(1 citation statement)

References 33 publications

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“…The AUROC of a classifier can be defined as the probability of the classifier ranking a randomly chosen positive example higher than a randomly chosen negative example [44] . Therefore, the higher the AUROC, the higher the predictive accuracy [45] . This study also used AUROC values for performance comparisons of different prediction models.…”

Section: Discussionmentioning

confidence: 99%

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Lee

Chen

et al. 2012

PLoS ONE

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BackgroundA database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group.MethodsThe three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models.ConclusionsThe ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection.

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Section: Discussionmentioning

confidence: 99%

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Lee

Chen

et al. 2012

PLoS ONE

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Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database

Yang

Wang

et al. 2018

Lipids Health Dis

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ObjectiveThis study aimed to provide an epidemiological model to evaluate the risk of developing dyslipidaemia within 5 years in the Taiwanese population.MethodsA cohort of 11,345 subjects aged 35–74 years and was non-dyslipidaemia in the initial year 1996 and followed in 1997–2006 to derive a risk score that could predict the occurrence of dyslipidaemia. Multivariate logistic regression was used to derive the risk functions using the check-up centre of the overall cohort. Rules based on these risk functions were evaluated in the remaining three centres as the testing cohort. We evaluated the predictability of the model using the area under the receiver operating characteristic (ROC) curve (AUC) to confirm its diagnostic property on the testing sample. We also established the degrees of risk based on the cut-off points of these probabilities after transforming them into a normal distribution by log transformation.ResultsThe incidence of dyslipidaemia over the 5-year period was 19.1%. The final multivariable logistic regression model includes the following six risk factors: gender, history of diabetes, triglyceride level, HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol) and BMI (body mass index). The ROC AUC was 0.709 (95% CI: 0.693–0.725), which could predict the development of dyslipidaemia within 5 years.ConclusionThis model can help individuals assess the risk of dyslipidaemia and guide group surveillance in the community.Electronic supplementary materialThe online version of this article (10.1186/s12944-018-0906-2) contains supplementary material, which is available to authorized users.

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Machine learning and systems genomics approaches for multi-omics data

2017

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In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

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Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms

Cited by 8 publications

References 33 publications

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database

Machine learning and systems genomics approaches for multi-omics data

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