Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent

Kurniawan, Isman; Rosalinda, Maya; Ikhsan, Nurul

doi:10.1080/1062936x.2020.1773534

Cited by 21 publications

(15 citation statements)

References 26 publications

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“…This algorithm can be used for categorical variables referred to in classification and continuous response referred to as regression. The samples data and randomly construct decision trees to avoid overfit from train data [12]. Random Forest's additional features include measuring variable importance, missing value imputation, outlier detection, etc [13].…”

Section: Prediction Modelmentioning

confidence: 99%

“…This algorithm's main idea is to use a weighted version from train data instead of random sampling [16]. It can adaptively adjust the weight from the classifier group and give a better result because of the diversity of each group [12]. The difference between Random Forest with XGBoost and AdaBoost is the tree in Random Forest work sequentially [13].…”

Section: Prediction Modelmentioning

confidence: 99%

“…ROC graph is constructed by the value of true positive rate and false-positive rate with the sample proportion. This model is also evaluated by using the area under the curve (AUC) [12].…”

Section: 𝑄 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁mentioning

confidence: 99%

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Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Purba

Nhita

Kurniawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively.

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Section: Prediction Modelmentioning

confidence: 99%

Section: Prediction Modelmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Purba

Nhita

Kurniawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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show abstract

“…RF is one of the popular machine learning methods that can handle high-dimensional data (Nembrini et al, 2018). RF builds a random decision tree using the bootstrap/bagging concept (SLRF, 2022;Ram et al, 2017;Kurniawan et al, 2020). RF performance is usually not affected by hyperparameter tuning (Logotheti et al, 2016).…”

Section: Prediction Modelmentioning

confidence: 99%

“…AdaBoost works by adjusting the weights for each cycle of the weak classifier group. AdaBoost can give better results because the diversity among classifiers is weak based on the performance of each classifier (Kurniawan et al, 2020).…”

Section: Prediction Modelmentioning

confidence: 99%

Classification of Non-Small Cell Lung Cancer Based on Gene Expression in Cases of Smokers and Non-Smokers using Ensemble Methods with Statistical Based Feature Selection

Nhita¹,

Kurniawan²

2022

Journal of Computer Science

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Lung cancer is one of the leading causes of death globally. One of the main risk factors for lung cancer is smoking, which causes more than 90% of lung cancer cases. There are two types of lung cancer, i.e., Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC), which the latter is the most common. One method that can be used to detect cancer is the implementation of machine learning on gene expression data. Machine learning is one approach that promises good performance in classifying gene expression data. This study aimed to predict the existence of NSCLC based on gene expression, whether including NSCLC or normal. We used three data sets, i.e., GSE10072, GSE19804, and GSE19188, which relate to the cases of NSCLC in smokers and nonsmokers. The prediction was carried out using six Ensemble Methods, i.e., Random Forest, Adaptive Boosting, Extra Tree, Gradient Boosting, Extreme Gradient Boosting, and Categorical Boosting. Feature selection was carried out by calculating the correlation between feature and target according to statistical parameters, i.e., ANOVA, Mutual Information (MI), and a combination of ANOVA and MI. We obtained the prediction model that outperformed the related studies for two similar data sets with the value of accuracy for the GSE10072, GSE19804, and GSE19188 data sets 100%, 97.22%, and 100%, respectively.

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Antiviral and antimicrobial applications of chalcones and their derivatives: From nature to greener synthesis

Nematollahi,

Mehrabani,

Hozhabri

et al. 2023

Heliyon

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Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent

Cited by 21 publications

References 26 publications

Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Classification of Non-Small Cell Lung Cancer Based on Gene Expression in Cases of Smokers and Non-Smokers using Ensemble Methods with Statistical Based Feature Selection

Antiviral and antimicrobial applications of chalcones and their derivatives: From nature to greener synthesis

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