scholarly journals Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2074
Author(s):  
Yuting Liu ◽  
Mengzhou Bi ◽  
Xuewen Zhang ◽  
Na Zhang ◽  
Guohui Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Inhibitory Activity ◽  
Predictive Accuracy ◽  
Information Gain ◽  
High Reliability ◽  
Learning Methods ◽  
Test Set ◽  
Machine Learning Methods ◽  
Ck2 Inhibitors

Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors.

scholarly journals Identification of Estrogen Receptor α Antagonists from Natural Products via In Vitro and In Silico Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaocong Pang ◽  
Weiqi Fu ◽  
Jinhua Wang ◽  
De Kang ◽  
Lvjie Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Estrogen Receptor ◽  
Natural Products ◽  
Molecular Docking ◽  
Luciferase Reporter ◽  
Luciferase Reporter Gene ◽  
Learning Methods ◽  
Test Set ◽  
Machine Learning Methods ◽  
Gene Assay

Estrogen receptor α (ERα) is a successful target for ER-positive breast cancer and also reported to be relevant in many other diseases. Selective estrogen receptor modulators (SERMs) make a good therapeutic effect in clinic. Because of the drug resistance and side effects of current SERMs, the discovery of new SERMs is given more and more attention. Virtual screening is a validated method to high effectively to identify novel bioactive small molecules. Ligand-based machine learning methods and structure-based molecular docking were first performed for identification of ERα antagonist from in-house natural product library. Naive Bayesian and recursive partitioning models with two kinds of descriptors were built and validated based on training set, test set, and external test set and then were utilized for distinction of active and inactive compounds. Totally, 162 compounds were predicted as ER antagonists and were further evaluated by molecular docking. According to docking score, we selected 8 representative compounds for both ERα competitor assay and luciferase reporter gene assay. Genistein, daidzein, phloretin, ellagic acid, ursolic acid, (−)-epigallocatechin-3-gallate, kaempferol, and naringenin exhibited different levels for antagonistic activity against ERα. These studies validated the feasibility of machine learning methods for predicting bioactivities of ligands and provided better insight into the natural products acting as estrogen receptor modulator, which are important lead compounds for future new drug design.

scholarly journals In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2892 ◽  
Author(s):  
Guohui Sun ◽  
Tengjiao Fan ◽  
Xiaodong Sun ◽  
Yuxing Hao ◽  
Xin Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Methyltransferase ◽  
Information Gain ◽  
Alkylating Agents ◽  
External Validation ◽  
Learning Methods ◽  
Methylguanine Dna Methyltransferase ◽  
Machine Learning Methods ◽  
Dragon Descriptors ◽  
External Test

O6-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O6-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q2Loo = 0.83, R2 = 0.87, Q2ext = 0.67, and R2ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors.

Assessing Replicability of Machine Learning Results: An Introduction to Methods on Predictive Accuracy in Social Sciences

2019 ◽  
pp. 089443931988844
Author(s):  
Ranjith Vijayakumar ◽  
Mike W.-L. Cheung
Keyword(s):  
Machine Learning ◽  
Empirical Data ◽  
Fixed Effects ◽  
Predictive Accuracy ◽  
Support Vector ◽  
Learning Methods ◽  
Data Set ◽  
Replication Studies ◽  
Machine Learning Methods ◽  
Accuracy Measure

Machine learning methods have become very popular in diverse fields due to their focus on predictive accuracy, but little work has been conducted on how to assess the replicability of their findings. We introduce and adapt replication methods advocated in psychology to the aims and procedural needs of machine learning research. In Study 1, we illustrate these methods with the use of an empirical data set, assessing the replication success of a predictive accuracy measure, namely, R 2 on the cross-validated and test sets of the samples. We introduce three replication aims. First, tests of inconsistency examine whether single replications have successfully rejected the original study. Rejection will be supported if the 95% confidence interval (CI) of R 2 difference estimates between replication and original does not contain zero. Second, tests of consistency help support claims of successful replication. We can decide apriori on a region of equivalence, where population values of the difference estimates are considered equivalent for substantive reasons. The 90% CI of a different estimate lying fully within this region supports replication. Third, we show how to combine replications to construct meta-analytic intervals for better precision of predictive accuracy measures. In Study 2, R 2 is reduced from the original in a subset of replication studies to examine the ability of the replication procedures to distinguish true replications from nonreplications. We find that when combining studies sampled from same population to form meta-analytic intervals, random-effects methods perform best for cross-validated measures while fixed-effects methods work best for test measures. Among machine learning methods, regression was comparable to many complex methods, while support vector machine performed most reliably across a variety of scenarios. Social scientists who use machine learning to model empirical data can use these methods to enhance the reliability of their findings.

scholarly journals Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Cindy Feng ◽  
George Kephart ◽  
Elizabeth Juarez-Colunga
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Predictive Accuracy ◽  
Classification Tree ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Learning Methods ◽  
Linear Discriminant ◽  
Machine Learning Methods ◽  
Extreme Gradient Boosting

Abstract Background Coronavirus disease (COVID-19) presents an unprecedented threat to global health worldwide. Accurately predicting the mortality risk among the infected individuals is crucial for prioritizing medical care and mitigating the healthcare system’s burden. The present study aimed to assess the predictive accuracy of machine learning methods to predict the COVID-19 mortality risk. Methods We compared the performance of classification tree, random forest (RF), extreme gradient boosting (XGBoost), logistic regression, generalized additive model (GAM) and linear discriminant analysis (LDA) to predict the mortality risk among 49,216 COVID-19 positive cases in Toronto, Canada, reported from March 1 to December 10, 2020. We used repeated split-sample validation and k-steps-ahead forecasting validation. Predictive models were estimated using training samples, and predictive accuracy of the methods for the testing samples was assessed using the area under the receiver operating characteristic curve, Brier’s score, calibration intercept and calibration slope. Results We found XGBoost is highly discriminative, with an AUC of 0.9669 and has superior performance over conventional tree-based methods, i.e., classification tree or RF methods for predicting COVID-19 mortality risk. Regression-based methods (logistic, GAM and LASSO) had comparable performance to the XGBoost with slightly lower AUCs and higher Brier’s scores. Conclusions XGBoost offers superior performance over conventional tree-based methods and minor improvement over regression-based methods for predicting COVID-19 mortality risk in the study population.

scholarly journals Detecting Phishing Websites Using an Efficient Feature-based Machine Learning Framework

2021 ◽  
Vol 11 (2) ◽  
pp. 2106-2112
Author(s):  
K. Mohana Sundaram ◽  
R. Sasikumar ◽  
Atthipalli Sai Meghana ◽  
Arava Anuja ◽  
Chandolu Praneetha
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Sensitive Information ◽  
Learning Methods ◽  
Modified Model ◽  
Learning Framework ◽  
Machine Learning Methods ◽  
Test Models ◽  
Digital Crime ◽  
Feature Based

Phishing is a form of digital crime where spam messages and spam sites attract users to exploit sensitive information on fishermen. Sensitive information obtained is used to take notes or to access money. To combat the crime of identity theft, Microsoft's cloud-based program attempts to use logical testing to determine how you can build trust with the characters. The purpose of this paper is to create a molded channel using a variety of machine learning methods. Separation is a method of machine learning that can be used effectively to identify fish, assemble and test models, use different mixing settings, and look at different mechanical learning processes, and measure the accuracy of the modified model and show multiple measurement measurements. The current study compares predictive accuracy, f1 scores, guessing and remembering multiple machine learning methods including Naïve Bayes (NB) and Random forest (RF) to detect criminal messages to steal sensitive information and improve the process by selecting highlighting strategies and improving crime classification accuracy. to steal sensitive information.

Replicability of Machine Learning Models in the Social Sciences

2018 ◽  
Vol 226 (4) ◽  
pp. 259-273 ◽  
Author(s):  
Ranjith Vijayakumar ◽  
Mike W.-L. Cheung
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Variable Selection ◽  
Model Building ◽  
Predictive Accuracy ◽  
Explanatory Power ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract. Machine learning tools are increasingly used in social sciences and policy fields due to their increase in predictive accuracy. However, little research has been done on how well the models of machine learning methods replicate across samples. We compare machine learning methods with regression on the replicability of variable selection, along with predictive accuracy, using an empirical dataset as well as simulated data with additive, interaction, and non-linear squared terms added as predictors. Methods analyzed include support vector machines (SVM), random forests (RF), multivariate adaptive regression splines (MARS), and the regularized regression variants, least absolute shrinkage and selection operator (LASSO), and elastic net. In simulations with additive and linear interactions, machine learning methods performed similarly to regression in replicating predictors; they also performed mostly equal or below regression on measures of predictive accuracy. In simulations with square terms, machine learning methods SVM, RF, and MARS improved predictive accuracy and replicated predictors better than regression. Thus, in simulated datasets, the gap between machine learning methods and regression on predictive measures foreshadowed the gap in variable selection. In replications on the empirical dataset, however, improved prediction by machine learning methods was not accompanied by a visible improvement in replicability in variable selection. This disparity is explained by the overall explanatory power of the models. When predictors have small effects and noise predominates, improved global measures of prediction in a sample by machine learning methods may not lead to the robust selection of predictors; thus, in the presence of weak predictors and noise, regression remains a useful tool for model building and replication.

scholarly journals Application of AI Modeling Technology Based on Fluid Biopsy to Diagnose AD

2021 ◽  
Author(s):  
Yuan Sh ◽  
Benliang Liu ◽  
Jianhu Zhang ◽  
Ying Zhou ◽  
Zhiyuan Hu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mrna Expression ◽  
Feature Engineering ◽  
Expression Data ◽  
Blood Biomarkers ◽  
Learning Methods ◽  
Test Set ◽  
Mrna Expression Data ◽  
Machine Learning Methods

Abstract BackgroundThere are no obvious clinical symptoms in the early stages of Alzheimer's disease (AD). Therefore, the diagnosis of AD directly leads to serious lag. Studies have shown that most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, the actual time of diagnosis of AD is much later than the time of onset. This brings great difficulties to the late treatment and management of patients. Therefore, early diagnosis of AD is very important. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to find the changes of blood transcriptome during the development of AD, and to search for potential blood biomarkers.MethodIndividualized blood mRNA expression data were downloaded from the GEO database in 711 patients, including control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, Xcell tool was used to analyze the blood mRNA expression data to obtain the composition and quantitative data of blood cells. Ratio characteristics were established for mRNA and Xcell data respectively. Feature engineering operations such as collinearity and importance analysis are performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.ResultA total of 5625 differential mRNAs were obtained by differential analysis of blood mRNAs. Through feature engineering screening, the best feature collection was obtained, and the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The AUC of CON, MCI and AD were 0.9746, 0.9536 and 0.9807, respectively. ConclusionThe 181 features are composed of four dimensions, which can accurately classify CON, MCI and AD groups, suggesting that machine learning methods can capture changes in blood biomarkers in Alzheimer's patients. The results of cell homeostasis analysis suggested that the homeostasis of NTK cells might be related to AD, and the homeostasis of GMP might be one of the reasons for AD.

scholarly journals Comparing regression modeling strategies for predicting hometime

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jessalyn K. Holodinsky ◽  
Amy Y. X. Yu ◽  
Moira K. Kapral ◽  
Peter C. Austin
Keyword(s):  
Machine Learning ◽  
Administrative Data ◽  
Statistical Models ◽  
Predictive Accuracy ◽  
Extreme Values ◽  
Future Research ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Models

Abstract Background Hometime, the total number of days a person is living in the community (not in a healthcare institution) in a defined period of time after a hospitalization, is a patient-centred outcome metric increasingly used in healthcare research. Hometime exhibits several properties which make its statistical analysis difficult: it has a highly non-normal distribution, excess zeros, and is bounded by both a lower and upper limit. The optimal methodology for the analysis of hometime is currently unknown. Methods Using administrative data we identified adult patients diagnosed with stroke between April 1, 2010 and December 31, 2017 in Ontario, Canada. 90-day hometime and clinically relevant covariates were determined through administrative data linkage. Fifteen different statistical and machine learning models were fit to the data using a derivation sample. The models’ predictive accuracy and bias were assessed using an independent validation sample. Results Seventy-five thousand four hundred seventy-five patients were identified (divided into a derivation set of 49,402 and a test set of 26,073). In general, the machine learning models had lower root mean square error and mean absolute error than the statistical models. However, some statistical models resulted in lower (or equal) bias than the machine learning models. Most of the machine learning models constrained predicted values between the minimum and maximum observable hometime values but this was not the case for the statistical models. The machine learning models also allowed for the display of complex non-linear interactions between covariates and hometime. No model captured the non-normal bucket shaped hometime distribution. Conclusions Overall, no model clearly outperformed the others. However, it was evident that machine learning methods performed better than traditional statistical methods. Among the machine learning methods, generalized boosting machines using the Poisson distribution as well as random forests regression were the best performing. No model was able to capture the bucket shaped hometime distribution and future research on factors which are associated with extreme values of hometime that are not available in administrative data is warranted.

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