Bioactive Molecule Prediction Using Extreme Gradient Boosting

Mustapha, Ismail B.; Saeed, Faisal

doi:10.3390/molecules21080983

Cited by 203 publications

(107 citation statements)

References 23 publications

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“…The proposed method is based on Logistic Regression (LR), an efficient machine learning technique [11,12,13]. In first phase, LR receives data from the ISEAR emotion dataset, and then emotion classification is performed by applying LR classifier.…”

Section: Methodsmentioning

confidence: 99%

Classifying Text-Based Emotions Using Logistic Regression

Alotaibi¹

2019

VAWKUM trans. comput. sci.

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Emotion detection textual content is getting popular among individuals and business companies to analyze user emotional reaction on the products they use. In this work, emotion detection from textual content is performed by using supervised learningbased Logistic Regression classifier. ISEAR dataset is used to taring the classifier, while testing dataset is used to evaluate the prediction capability of the classifier for emotion classification. The prior works used rule-based techniques, supported by lexical resources. However, limited coverage of emotional clues, was the major issue, which resulted in poor performance of system. The proposed work overcomes this limitation by proposing supervised learning technique using Logistic Regression classifier. The results obtained are encouraging and show that the proposed system performed better than the similar methods.

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Section: Methodsmentioning

confidence: 99%

Classifying Text-Based Emotions Using Logistic Regression

Alotaibi¹

2019

VAWKUM trans. comput. sci.

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“…Babajide Mustapha, et.al [7] proposed a model to predict the biological activity by using quantitative depiction of a compound's molecular composition. They used seven mainly familiar datasets of the literature and investigational results revealed that Xgboost smashed Naïve Bayes (NB), Support Vector Machines (LSVM), Random Forest (RF) and Radial Basis Function Neural Network (RBFN) in predicting the biological behavior.…”

Section: Related Workmentioning

confidence: 99%

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Murty¹,

Kumar²

2019

IJEAT

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Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets. Building an XGBoost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XGBoost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XGBoost model for the first time for Liver disease prediction and got 99% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.

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“…The methods used are predictive and reliable for predicting the activity of the new compound. For this purpose, five machine learning regression algorithms were built (i.e., XGboost Tree Ensemble, Random Forest, Support Vector Regression, Deep learning, and Multiple Linear Regression) and four machine learning classification algorithms (i.e., XGboost Tree Ensemble, Random Forest, Support Vector Machine, and Deep learning) [31,32].…”

Section: Introductionmentioning

confidence: 99%

Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes

Hermansyah

Bustamam

Yanuar

2020

Preprint

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Background: Dipeptidyl Peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus, but some classes of these drugs have side effects such as joint pain that can become severe to pancreatitis. It is thought that these side effects appear related to their inhibition against enzymes DPP-8 and DPP-9. Objective: This study aims to find DPP-4 inhibitor hit compounds that are selective against the DPP-8 and DPP-9 enzymes. By building a virtual screening workflow using the Quantitative Structure-Activity Relationship (QSAR) method based on artificial intelligence (AI), millions of molecules from the database can be screened for the DPP-4 enzyme target with a faster time compared to other screening methods. Result: Five regression machine learning algorithms and four classification machine learning algorithms were used to build virtual screening workflows. The algorithm that qualifies for the regression QSAR model was Support Vector regression with R 2 pred 0.78, while the classification QSAR model was Random Forest with 92.21% accuracy. The virtual screening results of more than 10 million molecules from the database, obtained 2,716 hit compounds with pIC50 above 7.5. Molecular docking results of several potential hit compounds to the DPP-4, DPP-8 and DPP-9 enzymes, obtained CH0002 hit compound that has a high inhibitory potential against the DPP-4 enzyme and low inhibition of the DPP-8 and DPP-9 enzymes. Conclusion: This research was able to produce DPP-4 inhibitor hit compounds that are potential to DPP-4 and selective to DPP-8 and DPP-9 enzymes so that they can be further developed in the DPP-4 inhibitors discovery. The resulting virtual screening workflow can be applied to the discovery of hit compounds on other targets.

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Bioactive Molecule Prediction Using Extreme Gradient Boosting

Cited by 203 publications

References 23 publications

Classifying Text-Based Emotions Using Logistic Regression

Classifying Text-Based Emotions Using Logistic Regression

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes

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