scholarly journals Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

2018 ◽  
Vol 20 (6) ◽  
pp. 2185-2199 ◽  
Author(s):  
Yanju Zhang ◽  
Ruopeng Xie ◽  
Jiawei Wang ◽  
André Leier ◽  
Tatiana T Marquez-Lago ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Post Translational Modification ◽  
K Nearest Neighbor ◽  
Ensemble Models ◽  
Light Gradient ◽  
Optimal Ensemble

AbstractAs a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Interpretable Machine Learning for Early Neurological Deterioration Prediction in Atrial Fibrillation-Related Stroke

2021 ◽  
Author(s):  
Seong Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi O ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multi-center prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.778, 95% CI, 0.726 - 0.830). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.

scholarly journals Bacterial Immunogenicity Prediction by Machine Learning Methods

Vaccines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 709
Author(s):  
Ivan Dimitrov ◽  
Nevena Zaharieva ◽  
Irini Doytchinova
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Predictive Ability ◽  
Initial Step ◽  
Majority Voting ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Test Set ◽  
Extreme Gradient Boosting

The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis, k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), random subspace method (RSM), and extreme gradient boosting) on a set of 317 known bacterial immunogens and 317 bacterial non-immunogens and derived models for immunogenicity prediction. The models were validated by internal cross-validation in 10 groups from the training set and by the external test set. All of them showed good predictive ability, but the xgboost model displays the most prominent ability to identify immunogens by recognizing 84% of the known immunogens in the test set. The combined RSM-kNN model was the best in the recognition of non-immunogens, identifying 92% of them in the test set. The three best performing ML models (xgboost, RSM-kNN, and RF) were implemented in the new version of the server VaxiJen, and the prediction of bacterial immunogens is now based on majority voting.

scholarly journals Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction

2020 ◽  
Vol 7 (2) ◽  
pp. 631-647
Author(s):  
Emrana Kabir Hashi ◽  
Md. Shahid Uz Zaman
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Traditional Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Traditional System ◽  
Prediction Approach

Machine learning techniques are widely used in healthcare sectors to predict fatal diseases. The objective of this research was to develop and compare the performance of the traditional system with the proposed system that predicts the heart disease implementing the Logistic regression, K-nearest neighbor, Support vector machine, Decision tree, and Random Forest classification models. The proposed system helped to tune the hyperparameters using the grid search approach to the five mentioned classification algorithms. The performance of the heart disease prediction system is the major research issue. With the hyperparameter tuning model, it can be used to enhance the performance of the prediction models. The achievement of the traditional and proposed system was evaluated and compared in terms of accuracy, precision, recall, and F1 score. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 85.25% and 91.80%. These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease with the acquisition of feasible performance.

scholarly journals Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Tahmina Nasrin Poly ◽  
Md.Mohaimenul Islam ◽  
Muhammad Solihuddin Muhtar ◽  
Hsuan-Chia Yang ◽  
Phung Anh (Alex) Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Prediction Models ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Alert Fatigue ◽  
Machine Learning Models

BACKGROUND Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far. OBJECTIVE Our objective was to develop machine learning prediction models to predict physicians’ responses in order to reduce alert fatigue from disease medication–related CDSSs. METHODS We collected data from a disease medication–related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets. RESULTS A total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively. CONCLUSIONS In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication–related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

scholarly journals Comparison of machine learning algorithms applied to symptoms to determine infectious causes of death in children: national survey of 18,000 verbal autopsies in the Million Death Study in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Susan Idicula-Thomas ◽  
Ulka Gawde ◽  
Prabhat Jha
Keyword(s):  
Machine Learning ◽  
Verbal Autopsy ◽  
Causes Of Death ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Diarrhoeal Diseases

Abstract Background Machine learning (ML) algorithms have been successfully employed for prediction of outcomes in clinical research. In this study, we have explored the application of ML-based algorithms to predict cause of death (CoD) from verbal autopsy records available through the Million Death Study (MDS). Methods From MDS, 18826 unique childhood deaths at ages 1–59 months during the time period 2004–13 were selected for generating the prediction models of which over 70% of deaths were caused by six infectious diseases (pneumonia, diarrhoeal diseases, malaria, fever of unknown origin, meningitis/encephalitis, and measles). Six popular ML-based algorithms such as support vector machine, gradient boosting modeling, C5.0, artificial neural network, k-nearest neighbor, classification and regression tree were used for building the CoD prediction models. Results SVM algorithm was the best performer with a prediction accuracy of over 0.8. The highest accuracy was found for diarrhoeal diseases (accuracy = 0.97) and the lowest was for meningitis/encephalitis (accuracy = 0.80). The top signs/symptoms for classification of these CoDs were also extracted for each of the diseases. A combination of signs/symptoms presented by the deceased individual can effectively lead to the CoD diagnosis. Conclusions Overall, this study affirms that verbal autopsy tools are efficient in CoD diagnosis and that automated classification parameters captured through ML could be added to verbal autopsies to improve classification of causes of death.

scholarly journals A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1692 ◽  
Author(s):  
Iván Silva ◽  
José Eugenio Naranjo
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Prediction Models ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
The Other ◽  
Support Vector ◽  
Classification Models ◽  
K Nearest Neighbor

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

scholarly journals Predicting Learning Outcomes with MOOC Clickstreams

2019 ◽  
Vol 9 (2) ◽  
pp. 104 ◽  
Author(s):  
Chen-Hsiang Yu ◽  
Jungpin Wu ◽  
An-Chi Liu
Keyword(s):  
Machine Learning ◽  
Learning Outcomes ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Video Data ◽  
Support Vector ◽  
Completion Rates ◽  
K Nearest Neighbor ◽  
Learning Behaviors

Massive Open Online Courses (MOOCs) have gradually become a dominant trend in education. Since 2014, the Ministry of Education in Taiwan has been promoting MOOC programs, with successful results. The ability of students to work at their own pace, however, is associated with low MOOC completion rates and has recently become a focus. The development of a mechanism to effectively improve course completion rates continues to be of great interest to both teachers and researchers. This study established a series of learning behaviors using the video clickstream records of students, through a MOOC platform, to identify seven types of cognitive participation models of learners. We subsequently built practical machine learning models by using K-nearest neighbor (KNN), support vector machines (SVM), and artificial neural network (ANN) algorithms to predict students’ learning outcomes via their learning behaviors. The ANN machine learning method had the highest prediction accuracy. Based on the prediction results, we saw a correlation between video viewing behavior and learning outcomes. This could allow teachers to help students needing extra support successfully pass the course. To further improve our method, we classified the course videos based on their content. There were three video categories: theoretical, experimental, and analytic. Different prediction models were built for each of these three video types and their combinations. We performed the accuracy verification; our experimental results showed that we could use only theoretical and experimental video data, instead of all three types of data, to generate prediction models without significant differences in prediction accuracy. In addition to data reduction in model generation, this could help teachers evaluate the effectiveness of course videos.

scholarly journals Application of Machine-Learning-Based Fusion Model in Visibility Forecast: A Case Study of Shanghai, China

2021 ◽  
Vol 13 (11) ◽  
pp. 2096
Author(s):  
Zhongqi Yu ◽  
Yuanhao Qu ◽  
Yunxin Wang ◽  
Jinghui Ma ◽  
Yu Cao
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Eastern China ◽  
Prediction Method ◽  
Sampling Technique ◽  
Environmental Modeling ◽  
Gradient Boosting ◽  
Fusion Model ◽  
Light Gradient ◽  
Extreme Gradient Boosting

A visibility forecast model called a boosting-based fusion model (BFM) was established in this study. The model uses a fusion machine learning model based on multisource data, including air pollutants, meteorological observations, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and an operational regional atmospheric environmental modeling System for eastern China (RAEMS) outputs. Extreme gradient boosting (XGBoost), a light gradient boosting machine (LightGBM), and a numerical prediction method, i.e., RAEMS were fused to establish this prediction model. Three sets of prediction models, that is, BFM, LightGBM based on multisource data (LGBM), and RAEMS, were used to conduct visibility prediction tasks. The training set was from 1 January 2015 to 31 December 2018 and used several data pre-processing methods, including a synthetic minority over-sampling technique (SMOTE) data resampling, a loss function adjustment, and a 10-fold cross verification. Moreover, apart from the basic features (variables), more spatial and temporal gradient features were considered. The testing set was from 1 January to 31 December 2019 and was adopted to validate the feasibility of the BFM, LGBM, and RAEMS. Statistical indicators confirmed that the machine learning methods improved the RAEMS forecast significantly and consistently. The root mean square error and correlation coefficient of BFM for the next 24/48 h were 5.01/5.47 km and 0.80/0.77, respectively, which were much higher than those of RAEMS. The statistics and binary score analysis for different areas in Shanghai also proved the reliability and accuracy of using BFM, particularly in low-visibility forecasting. Overall, BFM is a suitable tool for predicting the visibility. It provides a more accurate visibility forecast for the next 24 and 48 h in Shanghai than LGBM and RAEMS. The results of this study provide support for real-time operational visibility forecasts.

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