Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Chen, Cheng; Zhang, Qingmei; Yu, Bin; Yu, Zhaomin; Lawrence, Patrick J.; Ma, Qin; Zhang, Yan

doi:10.1016/j.compbiomed.2020.103899

Cited by 172 publications

(74 citation statements)

References 70 publications

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“…We adopted XGBoost and the stacking ensemble technique, which has been widely used to achieve better performance in many Kaggle competitions and research. 27,28 Recently, the combination of single XGBoost models using a stacking ensemble technique showed better performance than other machine learning algorithms did, which is consistent with our observations. 29 In our experience, the proposed ensemble technique showed superior performance to that of other state-of-the-art machine learning methods through trial and error.…”

Section: Discussionsupporting

confidence: 90%

Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens

Kang

Ryu

Lee³

et al. 2021

Trans. Vis. Sci. Tech.

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Selecting the optimal lens size by predicting the postoperative vault can reduce complications after implantation of an implantable collamer lens with a centralhole (ICL with KS-aquaport). We built a web-based machine learning application that incorporated clinical measurements to predict the postoperative ICL vault and select the optimal ICL size. Methods:We applied the stacking ensemble technique based on eXtreme Gradient Boosting (XGBoost) and a light gradient boosting machine to pre-operative ocular data from two eye centers to predict the postoperative vault. We assigned the Korean patient data to a training (N = 2756 eyes) and internal validation (N = 693 eyes) datasets (prospective validation). Japanese patient data (N = 290 eyes) were used as an independent external dataset from different centers to validate the model. Results:We developed an ensemble model that showed statistically better performance with a lower mean absolute error for ICL vault prediction (106.88 μm and 143.69 μm in the internal and external validation, respectively) than the other machine learning techniques and the classic ICL sizing methods did when applied to both validation datasets. Considering the lens size selection accuracy, our proposed method showed the best performance for both reference datasets (75.9% and 67.4% in the internal and external validation, respectively). Conclusions:Applying the ensemble approach to a large dataset of patients who underwent ICL implantation resulted in a more accurate prediction of vault size and selection of the optimal ICL size.Translational Relevance: We developed a web-based application for ICL sizing to facilitate the use of machine learning calculators for clinicians.

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Section: Discussionsupporting

confidence: 90%

Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens

Kang

Ryu

Lee³

et al. 2021

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

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“…As the publicly available virus-host PPI data increased, the emphasis on this subject has recently been shifted to machine-learning-based computational techniques to identify virus-host PPIs. PPI prediction tools have been developed based on different machine-learning models such as support vector machines (SVM) (Shen et al, 2007;Cui et al, 2012;Eid et al, 2016), random forest (RF) (Yang et al, 2020) and gradient boosting machine (XGBoost) (Basit et al, 2018;Chen et al, 2020).…”

Section: Machine Learning-based Ppi Prediction Toolsmentioning

confidence: 99%

ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor

Karabulut

Karpuzcu

Türk

et al. 2021

Front. Mol. Biosci.

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Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus–host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.

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“…To determine important features, the gain is used for the optimal node split during training in the tree-based classifiers such as RF, XGB, and GBC by Eq. (12) [43]:…”

Section: ) Feature Importance and Cumulative Feature Importancementioning

confidence: 99%

An Early Detection of Asthma Using BOMLA Detector

et al. 2021

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Asthma is a chronic and airway-induced disease, causing the incidence of bronchus inflammation, breathlessness, wheezing, is drastically becoming life-threatening. Even in the worst cases, it may destroy the quality to lead. Therefore, early detection of asthma is urgently needed, and machine learning can help identify asthma accurately. In this paper, a novel machine learning framework, namely BOMLA (Bayesian Optimisation-based Machine Learning framework for Asthma) detector has been proposed to detect asthma. Ten classifiers have been utilized in the BOMLA detector, where Support Vector Classifier (SVC), Random Forest (RF), Gradient Boosting Classifier (GBC), eXtreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) are state-of-the-art classifiers. In contrast, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLDA), Naive Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) are conventional popular classifiers. ADASYN algorithm has also been employed in the BOMLA detector to eradicate the issues created due to the imbalanced dataset. It has even been attempted to delineate how the ADASYN algorithm affects the classification performance. The highest accuracy (ACC) and Matthews's correlation coefficient (MCC) for an Asthma dataset provide 94.35% and 88.97%, respectively, using BOMLA detector when SVC is adapted, and it has been increased to 96.52% and 93.04%, respectively, when ensemble technique is adapted. The one-way analysis of variance (ANOVA) has also been performed in the 10-fold cross-validation to measure the statistical significance. A decision support system has been built as a potential application of the proposed system to visualize the probable outcome of the patient. Finally, it is expected that the BOMLA detector will help patients in their early diagnosis of asthma.INDEX TERMS Classification, Clinical and Non-clinical data, Asthma, ADASYN, ANOVA.

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Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Cited by 172 publications

References 70 publications

Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens

Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens

ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor

An Early Detection of Asthma Using BOMLA Detector

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