Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function

Atom type symmetry function that utilizes atom types defined in traditional force fields demonstrated improvements for describing structures of furanoses, and the capability of predicting their conformational adaptive charges with random forest regression models.

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Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

Genes ◽

10.3390/genes9020104 ◽

2018 ◽

Vol 9 (2) ◽

pp. 104 ◽

Cited By ~ 24

Author(s):

Aeriel Belk ◽

Zhenjiang Zech Xu ◽

David O. Carter ◽

Aaron Lynne ◽

Sibyl Bucheli ◽

...

Keyword(s):

Random Forest ◽

Regression Models ◽

Postmortem Interval ◽

Random Forest Regression ◽

Microbiome Data

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Applying Random Forest Model Algorithm to GFR Estimation

10.21203/rs.3.rs-74843/v1 ◽

2020 ◽

Author(s):

Peijia Liu ◽

Dong Yang ◽

Shaomin Li ◽

Yutian Chong ◽

Wentao Hu ◽

...

Keyword(s):

Random Forest ◽

Kidney Disease ◽

Linear Regression ◽

Regression Model ◽

Regression Models ◽

Random Forest Regression ◽

Variable Model ◽

Data Set ◽

Development Data ◽

Better Than

Abstract Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equationMethods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy , precision and root mean square error(RMSE).Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P<0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P<0.01, 19.08 vs 20.60, P<0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P=0.10, 0.8 vs 0.78, P=0.19, respectively).Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

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Approximating Prediction Uncertainty for Random Forest Regression Models

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.82.3.189 ◽

2016 ◽

Vol 82 (3) ◽

pp. 189-197 ◽

Cited By ~ 27

Author(s):

John W. Coulston ◽

Christine E. Blinn ◽

Valerie A. Thomas ◽

Randolph H. Wynne

Keyword(s):

Random Forest ◽

Regression Models ◽

Random Forest Regression ◽

Prediction Uncertainty

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Applying Random Forest Model Algorithm to GFR estimation

10.21203/rs.3.rs-22422/v1 ◽

2020 ◽

Author(s):

Peijia Liu ◽

Dong Yang ◽

Shaomin Li ◽

Yutian Chong ◽

Ming Li ◽

...

Keyword(s):

Random Forest ◽

Kidney Disease ◽

Linear Regression ◽

Regression Model ◽

Regression Models ◽

Random Forest Regression ◽

Variable Model ◽

Data Set ◽

Development Data ◽

Better Than

Abstract Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equation Methods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy, precision and root mean square error(RMSE). Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P < 0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P < 0.01, 19.08 vs 20.60, P < 0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P = 0.10, 0.8 vs 0.78, P = 0.19, respectively). Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

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Investigation of Random Subspace and Random Forest Regression Models Using Data with Injected Noise

Lecture Notes in Computer Science - Knowledge Engineering, Machine Learning and Lattice Computing with Applications ◽

10.1007/978-3-642-37343-5_1 ◽

2013 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Tadeusz Lasota ◽

Zbigniew Telec ◽

Bogdan Trawiński ◽

Grzegorz Trawiński

Keyword(s):

Random Forest ◽

Regression Models ◽

Random Subspace ◽

Random Forest Regression ◽

Using Data

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Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China

Remote Sensing ◽

10.3390/rs13163123 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3123

Author(s):

Chunzhu Wei ◽

Qianying Zhao ◽

Yang Lu ◽

Dongjie Fu

Keyword(s):

Random Forest ◽

Linear Regression ◽

Regression Model ◽

Pearl River Delta ◽

Regression Models ◽

River Delta ◽

Pearl River ◽

Linear Regression Models ◽

Random Forest Regression ◽

Band Combinations

Pearl River Delta (PRD), as one of the most densely populated regions in the world, is facing both natural changes (e.g., sea level rise) and human-induced changes (e.g., dredging for navigation and land reclamation). Bathymetric information is thus important for the protection and management of the estuarine environment, but little effort has been made to comprehensively evaluate the performance of different methods and datasets. In this study, two linear regression models—the linear band model and the log-transformed band ratio model, and two non-linear regression models—the support vector regression model and the random forest regression model—were applied to Landsat 8 (L8) and Sentinel-2 (S2) imagery for bathymetry mapping in 2019 and 2020. Results suggested that a priori area clustering based on spectral features using the K-means algorithm improved estimation accuracy. The random forest regression model performed best, and the three-band combinations outperformed two-band combinations in all models. When the non-linear models were applied with three-band combination (red, green, blue) to L8 and S2 imagery, the Root Mean Square Error (Mean Absolute Error) decreased by 23.10% (35.53%), and the coefficient of determination (Kling-Gupta efficiency) increased by 0.08 (0.09) on average, compared to those using the linear regression models. Despite the differences in spatial resolution and band wavelength, L8 and S2 performed similarly in bathymetry estimation. This study quantified the relative performance of different models and may shed light on the potential combination of multiple data sources for more timely and accurate bathymetry mapping.

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A Machine Learning based approach to unleash the impact of COVID-19 on Indian Stock Market

10.21203/rs.3.rs-54882/v1 ◽

2020 ◽

Author(s):

Nusrat Rouf ◽

Majid Bashir Malik ◽

Tasleem Arif

Keyword(s):

Machine Learning ◽

Random Forest ◽

Regression Model ◽

Regression Models ◽

Pearson Correlation ◽

Mean Square ◽

Random Forest Regression ◽

The World ◽

Closing Price ◽

The Impact

Abstract Introduction: Advancement in information technology, be it hardware, software or communication technology, over few decades has rapidly impacted almost every field of study. Machine learning tools and techniques are nowadays applied to every field. It has opened the ways for interdisciplinary research by promising effective analyzation and decision-making strategies. COVID-19 has badly affected more than 200 countries within a short span of time. It has drastically affected both daily activities as well as economic activities. Herd behavior of investors has triggered panic selling. As a result, stock markets around the world have plunged down.Methods: In this paper, we analyze the impact of COVID-19 on NSE (National Stock Exchange) index Nifty50. We employ Pearson Correlation and investigate the impact of total confirmed cases and daily cases on Nifty50 closing price. We use various machine learning regression models for predictive analysis viz, linear regression with polynomial terms (quadratic, cubic), Decision Tree Regression and Random Forest Regression. Model performance is measured using MSE (Mean Square Error), RMSE (Root Mean Square Error) and R2 (R Squared) evaluators. Results: Correlation analysis reveals that total confirmed cases and daily cases in both India and the World have negative correlation with Nifty50 closing prices. Moreover, Nifty50 closing prices are more negatively correlated with total confirmed and daily cases in India. Predictive analysis shows that the Random Forest Regression model outperforms all other models. Conclusion: We analyze and predict the impact of COVID-19 on closing price of Nifty50 index. We employ Pearson Correlation and investigate the impact of COVID-19 on Nifty50 closing prices. We use various machine learning regression models to predict the closing price of Nifty50 index. Results reveal that the market volatility is directly proportional to increase in number of COVID-19 cases. Random Forest Regression model has comparatively shown better RMSE and R2 values.

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A Random Forest Regression Model Predicting the Winners of Summer Olympic Events

Proceedings of the 2020 2nd International Conference on Big Data Engineering ◽

10.1145/3404512.3404513 ◽

2020 ◽

Author(s):

Mengjie Jia ◽

Yue Zhao ◽

Furong Chang ◽

Bofeng Zhang ◽

Kenji Yoshigoe

Keyword(s):

Random Forest ◽

Regression Model ◽

Random Forest Regression

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A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

Scientific Reports ◽

10.1038/s41598-021-94449-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chinmay P. Swami ◽

Nicholas Lenhard ◽

Jiyeon Kang

Keyword(s):

Machine Learning ◽

Random Forest ◽

Upper Limb ◽

Daily Living ◽

Machine Learning Algorithms ◽

Data Sets ◽

Random Forest Regression ◽

Prosthetic Devices ◽

Upper Limb Function ◽

The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

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