Improved feature-based prediction of SNPs in human cytochrome P450 enzymes

Li, Li; Xiong, Yi; Zhang, Zhuoyu; Guo, Quan; Xu, Qin; Liow, Hien-haw; Zhang, Yonghong; Wei, Dong‐Qing

doi:10.1007/s12539-014-0257-2

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…The following four metrics are commonly used in literature to measure the quality of binary classification (Xiong et al, 2012 ; Li et al, 2015 ): sensitivity, specificity, accuracy and Matthews' correlation coefficient (MCC), which are expressed as…”

Section: Methodsmentioning

confidence: 99%

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

2018

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Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

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Section: Methodsmentioning

confidence: 99%

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

2018

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“…It is because imbalanced-class data exist in this study (e.g., 1208 (6%) for UPRA vs. 20,684 (94%) for non-UPRA). High accuracies rates with imbalanced SENS and SPEC are expected in imbalanced-class data using the traditional approaches [ 18 , 19 , 20 , 21 ]. Thus, we applied the minimization of average model residuals in both classes (i) to obtain balanced SENS and SPEC and (ii) to overcome the disadvantage of high accuracy rates (i.e., the minimum residuals minimized by the formula of average (residuals in UPRA) + average(residuals in non-UPRA)).…”

Section: Methodsmentioning

confidence: 99%

“…For instance, Wang et al [ 16 ] developed a real-time model using the time series of vital signs and discrete features, such as laboratory tests. However, this model’s prediction accuracy was not sufficiently high (area under the receiver operating characteristic curve (AUC) = 0.70) [ 17 ] to deploy the model in the hospital information system with the proposed forecasting algorithms to support treatment because many false-positive cases appear in these imbalanced-class data [ 18 , 19 , 20 , 21 ], increasing the clinicians’ burden.…”

Section: Introductionmentioning

confidence: 99%

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

Tey

Chien

Hsu

et al. 2021

IJERPH

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Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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“…The balanced-class data were another important issue that should be considered. Otherwise, the imbalanced-class data [ 24 , 25 ] lead to an extremely imbalanced ratio (= SENS/SPEC or SPEC/SENS) while the modle pursuits the ultimate accurate rate of prediction (i.e., by minimizing the residuals). In this study.…”

Section: Methodsmentioning

confidence: 99%

Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study

Chou

Chien

Yang

et al. 2021

IJERPH

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The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study is to build an ANN model with an app for automatic prediction and classification of HOF for NBA players. We downloaded 4728 NBA players’ data of career stats and accolades from the website at basketball-reference.com. The training sample was collected from 85 HOF members and 113 retired Non-HOF players based on completed data and a longer career length (≥15 years). Featured variables were taken from the higher correlation coefficients (<0.1) with HOF and significant deviations apart from the two HOF/Non-HOF groups using logistical regression. Two models (i.e., ANN and convolutional neural network, CNN) were compared in model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC). An app predicting HOF was then developed involving the model’s parameters. We observed that (1) 20 feature variables in the ANN model yielded a higher AUC of 0.93 (95% CI 0.93–0.97) based on the 198-case training sample, (2) the ANN performed better than CNN on the accuracy of AUC (= 0.91, 95% CI 0.87–0.95), and (3) an ready and available app for predicting HOF was successfully developed. The 20-variable ANN model with the 53 parameters estimated by the ANN for improving the accuracy of HOF has been developed. The app can help NBA fans to predict their players likely to be inducted into the HOF and is not just limited to the active NBA players.

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Improved feature-based prediction of SNPs in human cytochrome P450 enzymes

Cited by 9 publications

References 30 publications

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study

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