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Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships [article]

Robert P. Sheridan, Andy Liaw, Matthew Tudor
2021 arXiv   pre-print
Here we compare Light Gradient Boosting Machine (LightGBM) to random forest, single-task deep neural nets, and Extreme Gradient Boosting (XGBoost) on 30 in-house data sets.  ...  Another very useful feature of LightGBM is that it includes a native method for estimating prediction intervals.  ...  INTRODUCTION Quantitative Structure-Activity Relationships (QSAR) models are very useful in the pharmaceutical industry for predicting on-target and off-target activities.  ... 
arXiv:2105.08626v1 fatcat:s46fui2eurahjaiyipiszzqsqu

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Yasunari Matsuzaka, Yoshihiro Uesawa
2020 Current Issues in Molecular Biology  
The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as in silico computational assessment for a long time.  ...  Further, owing to the high-performance modeling of QSAR, machine learning methods have been developed and upgraded.  ...  To date, machine learning algorithms, such as random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (Xgboost), Light Gradient Boosting Machine (LightGBM), Category Boosting (CATBoost  ... 
doi:10.21775/cimb.042.455 pmid:33339777 fatcat:3psyj34jwrbdbk4r5ljij6piri

Review of machine learning algorithms' application in pharmaceutical technology
Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji

Jelena Đuriš, Ivana Kurćubić, Svetlana Ibrić
2021 Arhiv za farmaciju  
Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described.  ...  The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions  ...  Light gradient boosting machine algorithm (lightGBM), as a high-performance boosting decision tree was used to predict complexation between cyclodextrins and active pharmaceutical ingredients (38) , as  ... 
doi:10.5937/arhfarm71-32499 fatcat:zikjqg64l5hd3javwa7piuy7hu

A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach

Ricardo Costa-Mendes, Tiago Oliveira, Mauro Castelli, Frederico Cruz-Jesus
2020 Education and Information Technologies : Official Journal of the IFIP technical committee on Education  
A multilinear regression model is used in parallel with random forest, support vector machine, artificial neural network and extreme gradient boosting machine stacking ensemble implementations.  ...  This article uses an anonymous 2014-15 school year dataset from the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Ministry of Education as a means to carry out a  ...  The extreme gradient boosting machine (Chen and Guestrin 2016) is a regularised version of the gradient boosting machine.  ... 
doi:10.1007/s10639-020-10316-y fatcat:uotpj6owtjb2rmoax34zjrlr6a

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Upma Singh, Mohammad Rizwan, Muhannad Alaraj, Ibrahim Alsaidan
2021 Energies  
In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree,  ...  The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.  ...  Data Availability Statement: The scrutiny of data and forecasting were accomplished with the openly available dataset that has been collected from the SCADA system for wind turbines in the northwestern  ... 
doi:10.3390/en14165196 fatcat:6xgb7bb5uvdpvabfzchctilzaq

PREDICTIVE ANALYSIS OF OPIOD AND NON-OPIOD PRESCRIBER FOR IMPROVING ACCURACY USING IMPROVE XGBOOSTING SYSTEM

Anjali, Shivendra Dubey, Rakesh Shivhare, Mukesh Dixit
2018 Journal of Harmonized Research in Applied Science  
Gradient boosting is an intense machine learning method presented by Friedman [2] .  ...  The loss function L: Y × A → R+ gives a quantitative measure of the loss brought about from picking activity a when the genuine  ...  Tree Boosting Methods: Using trees as base models for boosting is a very popular choice.  ... 
doi:10.30876/johr.6.4.2018.276-285 fatcat:35no2fac2zcrfoy7unnajs7cii

Prediction of Student Performance using Machine Learning

2019 International Journal of Engineering and Advanced Technology  
Consistently countless alumni from schools and colleges, as for the information gathered from the criticism of students, order an information mining strategy is connected to it.  ...  Educational foundations are delivering capable and shrewd understudies and specialists, yet when we think about quality and value of the student's advancement in his profession; it is as yet a challenge  ...  Machine Learning Methods AI calculations are frequently ordered as supervised or unsupervised.  ... 
doi:10.35940/ijeat.e7520.088619 fatcat:knbxstixlrbe5p7f2yuvl3poni

n Artificial Intelligence Approach Based on Hybrid CNN-XGB Model to Achieve High Prediction Accuracy through Feature Extraction, Classification and Regression for Enhancing Drug Discovery in Biomedicine

Mukesh Madanan, Biju T. Sayed, Nurul Akhmal Mohd Zulkefli, Nitha C. Velayudhan
2021 International Journal of Biology and Biomedical Engineering  
as the input data of the XGBoost for drug response prediction.  ...  Herein, the paper proposes a deep neural network structure as the Convolutional Neural Network (CNN) to detain the gene expression features of the cell line and then use the resulting abstract features  ...  k as, Gradient boosting is a system designed for regression and classification in machine learning.  ... 
doi:10.46300/91011.2021.15.22 fatcat:iu6ujquoarba3aqhph32s5pina

Machine Learning Methods and Qualimetric Approach to Determine the Conditions for Train Students in the Field of Environmental and Economic Activities

Artem Salamatov, Elena Gafarova, Vladimir Belevitin, Maxim Gafarov, Darya Gordeeva
2021 International Journal of Emerging Technologies in Learning (iJET)  
Among these tools are machine learning methods and mathematical models built on their basis for quantitative assessment of the quality of vocational training in the field of environmental and economic  ...  While mathematical modeling allows one to quickly adjust the organizational and pedagogical conditions as a set of opportunities for content, forms, teaching methods, information and communication technologies  ...  Acknowledgement The study was carried out with the financial support of the Russian Foundation for Basic Research within the framework of scientific project No. 19-29-07209.  ... 
doi:10.3991/ijet.v16i03.17715 fatcat:m2rab4yisrhjfedcxqkc4zo4ca

Predicting Blood–Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders

Fabien Plisson, Andrew Piggott
2019 Marine Drugs  
We evaluated several regression and classification models, and found that our optimised classifiers (random forest, gradient boosting, and logistic regression) outperformed other models, with overall cross-validated  ...  In this study, we revisited these marine natural products and predicted their ability to cross the BBB by applying freely available open-source chemoinformatics and machine learning algorithms to a training  ...  Capon and Nicholas Hamilton, both professors at the Institute for Molecular Bioscience, The University of Queensland for their early support and supervision.  ... 
doi:10.3390/md17020081 fatcat:lbpebeo6qzeevizc2ufwd4qt5y

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data

Vanessa E. Gray, Ronald J. Hause, Jens Luebeck, Jay Shendure, Douglas M. Fowler
2018 Cell Systems  
Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning  ...  Criteria for inclusion of deep mutational scanning data sets are described in the METHOD DETAILS section of the STAR Methods.  ...  Acknowledgments We thank Bill Noble and Christine Queitsch for insightful comments. This research was supported by research grants from the National Science Foundation to V.E.G. [DGE-1256082]  ... 
doi:10.1016/j.cels.2017.11.003 pmid:29226803 pmcid:PMC5799033 fatcat:d7g7sfvgmnay3lvuffvin22ukq

Machine Learning-Assisted High-Throughput Semi-empirical Search of OFET Molecular Materials [article]

Zhenyu Chen, Jiahao Li, Yuzhi Xu
2021 arXiv   pre-print
Light Gradient Boosting Machine (LightGBM) model's intrinsic Distributed and efficient features enables much faster training process and higher training efficiency, which means better model performance  ...  Here, we propose a deepth first search traversal (DFST) approach combined with lightGBM machine learning model to search the classic Organic field-effect transistor (OFET) functional molecules chemical  ...  Lianjie Zhang at SCUT for helpful discussion.  ... 
arXiv:2107.02613v1 fatcat:qevy46lojvbenjql3idft3k4vu

A Ensemble Machine Learning Based System for Merchant Credit Risk Detection in Merchant Mcc Misuse

Chih-Hsiung Su, Fengjun Tu, Xinyu Zhang, Ben-Chang Shia, Tian-Shyug Lee
2021 Journal of Data Science  
The present study aimed to develop and deploy an MCC misuse detection system with ensemble models, gives insights into the development process and compares different machine learning methods.  ...  The paper can thus not only suggest the MCC misuse cannot be overlooked but also help researchers and practitioners to apply new ensemble machine learning based detection system or similar problems.  ...  Light Gradient Boosting Machine (LightGBM) a gradient boosting framework that uses tree-based learning algorithms.  ... 
doi:10.6339/jds.201901_17(1).0004 fatcat:7birii5wrvhbdode5fgxxnjgcq

A Temporal-Spatial Model Based Short-Term Power Load Forecasting Method in COVID-19 Context

Bowen Liu, Da Xu, Lin Jiang, Shuangyin Chen, Yong He
2022 Frontiers in Energy Research  
The forecasting values of load demand can then be acquired by combining forecasted IMFs from light Gradient Boosting Machine (LightGBM) algorithm.  ...  This paper proposes a short-term load forecasting method in COVID-19 context based on temporal-spatial model.  ...  Various boosting algorithms inspired by gradient lifting decision tree (GBDT), including extreme gradient lifting (XGBoost) (Chen and Guestrin, 2016) and light Gradient Boosting Machine (LightGBM) (  ... 
doi:10.3389/fenrg.2022.923311 fatcat:s7rvpsvu2rhxhemjjmmhc6mnwu

Supervised Machine Learning Techniques: An Overview with Applications to Banking [article]

Linwei Hu, Jie Chen, Joel Vaughan, Hanyu Yang, Kelly Wang, Agus Sudjianto, Vijayan N. Nair
2020 arXiv   pre-print
The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural Networks (NNs). We begin with an introduction to ML tasks and techniques.  ...  This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking.  ...  Acknowledgments The authors are grateful to Xiaoyu Liu for her contributions to this work while she was at Wells Fargo.  ... 
arXiv:2008.04059v1 fatcat:7ynjllerszg6hbopkmkag5ydky
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