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29 papers · ranked by Valyu relevance

01
PPubMed13 Nov 2023
Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework
Ahmed M. Elshewey, Mahmoud Y. Shams, Sayed M. Tawfeek, Amal H. Alharbi + 8 more
'Amal H. Alharbi' 'Abdelhameed Ibrahim' 'Abdelaziz A. Abdelhamid' 'Marwa M. Eid' 'Nima Khodadadi' 'Laith Abualigah' 'Doaa Sami Khafaga' 'Zahraa Tarek' 'Kathiravan Srinivasan'] The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence…
92relevance
02
AarXiv2023
Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods
Abdollah Masoud Darya, Ilias Fernini, Marley Vellasco, Abir Hussain
—The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. Most recent contributions in the field of…
88relevance
03
AarXiv2023
Benchmarking state-of-the-art gradient boosting algorithms for classification
Piotr Florek, Adam Zagdański
This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-theart gradient boosting frameworks (i.e. XGBoost, LightGBM and CatBoost), have been thoroughly compared on several publicly available real-world…
85relevance
04
PPubMed20 May 2022
An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things
Deepti Rani, Nasib Singh Gill, Preeti Gulia, Jyotir Moy Chatterjee
Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and…
83relevance
05
PPubMed20 Mar 2026
Forecasting auditor’s going concern opinion using with hybrid robust machine learning model Forecasting auditor’s going concern opinion using with hybrid robust machine learning model
Uğur Ejder, Alpaslan Yaşar, Aamna AlShehhi
The importance of forecasting company bankruptcies makes the auditor’s reporting of the going concern opinion (GCO) a focal point for interested parties. Therefore, researchers have recently turned to predicting GCO using various machine learning (ML) methods. The aim of this research is to propose a novel hybrid model…
83relevance
06
AarXiv2019
Gradient Boosting Machine: A Survey
Zhiyuan He, Danchen Lin, Thomas Lau, Mike Wu
Proposed by Freund and Schapire (1997), boosting is a general issue of constructing an extremely accurate prediction with numerous roughly accurate predictions. Addressed by Friedman (2001, 2002) and Natekin and Knoll (2013), the Gradient Boosting Machines (GBM) seeks to build predictive models through back-fittings…
83relevance
07
PPubMed21 Sep 2015
Boosting for high-dimensional two-class prediction
Rok Blagus, Lara Lusa
Background In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly exceeds the number of samples) the choice of an appropriate classifier is crucial as it was…
82relevance
08
PPubMed11 Nov 2021
An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network
Chuan-Yu Chang, Sweta Bhattacharya, P. M. Durai Raj Vincent, Kuruva Lakshmanna + 1 more
'Kuruva Lakshmanna' 'Kathiravan Srinivasan'] The cry is a loud, high pitched verbal communication of infants. The very high fundamental frequency and resonance frequency characterize a neonatal infant cry having certain sudden variations. Furthermore, in a tiny duration solitary utterance, the cry signal also possesses…
81relevance
09
PPubMed28 Aug 2025
Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare Machine learning models for accurate liver disease prediction in healthcare
W. El Atifi, O. El Rhazouani, Fida Muhammad Khan, H. Sekkat + 1 more
'Anwar P.P. Abdul Majeed'] Liver disease encompasses a range of conditions affecting the liver, including hepatitis, cirrhosis, fatty liver, and liver cancer. It can be caused by infections, alcohol abuse, obesity, or genetic factors, and it often progresses silently until advanced stages. Early detection and lifestyle…
79relevance
10
PPubMed29 May 2024
An Ensemble Classifiers for Improved Prediction of Native–Non-Native Protein–Protein Interaction
Nor Kumalasari Caecar Pratiwi, Hilal Tayara, Kil To Chong, Bono Lučić
'Bono Lučić'] In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models…
79relevance
11
BbioRxiv8 Jul 2021
Efficient gradient boosting for prognostic biomarker discovery
Kaiqiao Li, Sijie Yao, Zhenyu Zhang, Biwei Cao + 4 more
Gradient boosting decision tree (GBDT) is a powerful ensemble machine learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as Extreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB), have rendered the GBDT training…
79relevance
12
BbioRxiv5 Feb 2019
Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry
Rohan Khera, Julian Haimovich, Nate Hurley, Robert McNamara + 7 more
Accurate prediction of risk of death following acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making. Contemporary machine-learning may improve risk-prediction by identifying complex relationships between predictors and outcomes. We studied 993,905 patients in the American…
75relevance
13
BbioRxiv4 Aug 2024
Topological embedding and directional feature importance in ensemble classifiers for multi-class classification
Eloisa Rocha Liedl, Shabeer Mohamed Yassin, Melpomeni Kasapi, Joram M. Posma
Cancer is the second leading cause of disease-related death worldwide, and machine learning-based identification of novel biomarkers is crucial for improving early detection and treatment of various cancers. A key challenge in applying machine learning to high-dimensional data is deriving important features in an…
75relevance
14
BbioRxiv24 Aug 2020
Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine
Lingling Yue, Minghui Wang, Xinhua Yang, Yu Han + 2 more
The identification of fertility-related proteins plays an essential part in understanding the embryogenesis of germ cell development. Since the traditional experimental methods are expensive and time-consuming to identify fertility-related proteins, the purposes of predicting protein functions from amino acid sequences…
74relevance
15
BbioRxiv10 Feb 2024
Advancing ecological networks: moving beyond binary classification to probabilistic interactions
Martin Philippe-Lesaffre
Leveraging trophic interactions to deduce macro-ecological patterns has become a prevalent method, taking advantage of the extensive databases on binary trophic interactions (i.e., prey-predator relationships). However, this binary approach oversimplifies complex ecological dynamics and fails to capture the nuanced…
74relevance
16
CchemRxiv14 Jul 2025
Machine Learning Analysis of Molecular Dynamics Properties Influencing Drug Solubility
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
71relevance
17
CchemRxiv15 Nov 2022
Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives
Itamar Borges Jr, Júlio César Duarte, Romulo Dias da Rocha
We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, ∑▒〖Q_0 (NO_2)〗 (the charge of the nitro groups)…
71relevance
18
AarXiv6 Feb 2022
Boosted Decision Trees
Y. Coadou
Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the highenergy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described. Some of their shortcomings are then…
70relevance
19
CchemRxiv24 Oct 2025
XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
69relevance
20
CchemRxiv25 Oct 2024
Physics-based Machine Learning to Predict Hydration Free Energies for Small Molecules with a minimal number of descriptors: Interpretable and Accurate
Authors not listed
Hydration free energy (HFE) of molecules is a fundamental property having impor- tance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Ma- chine learning (ML) models are increasingly being used to predict HFE.…
69relevance
21
AarXiv2019
Boosting Classifiers with Noisy Inference
Yongjune Kim, Yuval Cassuto, Lav R. Varshney
—We present a principled framework to address resource allocation for realizing boosting algorithms on substrates with communication or computation noise. Boosting classifiers (e.g., AdaBoost) make a final decision via a weighted vote fro m the outputs of many base classifiers (weak classifiers). Suppose that the base…
68relevance
22
BbioRxiv30 Jun 2026
Impacts of batch effects on the performance of machine learning classifiers across multiple studies
Preston Raab, W. Evan Johnson, Stephen R. Piccolo
Precision medicine relies on accurate and generalizable predictions for patients across the spectrum of human diversity. Because capturing biological heterogeneity requires large sample sizes, researchers must often aggregate data from several experimental batches or independent studies. This integration allows for…
68relevance
23
AarXiv2016
Vote-boosting ensembles
Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that…
65relevance
24
AarXiv2018
Enhanced version of AdaBoostM1 with J48 Tree learning method
Kyongche Kang, Jack Michalak
Machine Learning focuses on the construction and study of systems that can learn from data. This is connected with the classification problem, which usually is what Machine Learning algorithms are designed to solve. When a machine learning method is used by people with no special expertise in machine learning, it is…
65relevance
25
CchemRxiv6 May 2025
An Efficient Machine Learning-Based Prediction Model for JAK2 Inhibitor pIC50
Authors not listed
Background: Janus Kinase 2 (JAK2) is a key kinase in cellular signal transduction. Its abnormal activation is closely related to various myeloproliferative neoplasms and inflammatory diseases. Developing selective JAK2 inhibitors is an important direction in drug discovery. Accurate prediction of compound inhibitory…
61relevance
26
CchemRxiv1 Jun 2022
Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
59relevance
27
BbioRxiv18 May 2017
Evaluation of classifier performance for multiclass phenotype discrimination in untargeted metabolomics
Patrick J. Trainor, Andrew P. DeFilippis, Shesh N. Rai
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes and for furnishing diagnostic and prognostic phenotype predictions in medicine. Despite this, a comprehensive and rigorous evaluation of classification techniques for phenotype…
57relevance
28
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
53relevance
29
CchemRxiv18 Oct 2022
Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts
Kunal Lodaya, Nathan Ricke, Kelly Chen, Troy Van Voorhis
Graphite-conjugated catalysts (GCCs) are a promising class of materials that combine many of the advantages of heterogenous and homogeneous catalysts. In particular, GCCs containing an aryl-pyridinium active site appear to be effective nonmetal catalysts for the oxygen reduction reaction (ORR). In this study, we…
53relevance

Results for Gradient boosting classifiers

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods

Benchmarking state-of-the-art gradient boosting algorithms for classification

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Forecasting auditor’s going concern opinion using with hybrid robust machine learning model Forecasting auditor’s going concern opinion using with hybrid robust machine learning model

Gradient Boosting Machine: A Survey

Boosting for high-dimensional two-class prediction

An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network

Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare Machine learning models for accurate liver disease prediction in healthcare

An Ensemble Classifiers for Improved Prediction of Native–Non-Native Protein–Protein Interaction

Efficient gradient boosting for prognostic biomarker discovery

Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry

Topological embedding and directional feature importance in ensemble classifiers for multi-class classification

Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

Advancing ecological networks: moving beyond binary classification to probabilistic interactions

Machine Learning Analysis of Molecular Dynamics Properties Influencing Drug Solubility

Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives

Boosted Decision Trees

XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT

Physics-based Machine Learning to Predict Hydration Free Energies for Small Molecules with a minimal number of descriptors: Interpretable and Accurate

Boosting Classifiers with Noisy Inference

Impacts of batch effects on the performance of machine learning classifiers across multiple studies

Vote-boosting ensembles

Enhanced version of AdaBoostM1 with J48 Tree learning method

An Efficient Machine Learning-Based Prediction Model for JAK2 Inhibitor pIC50

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Evaluation of classifier performance for multiclass phenotype discrimination in untargeted metabolomics

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods

Benchmarking state-of-the-art gradient boosting algorithms for classification

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Forecasting auditor’s going concern opinion using with hybrid robust machine learning model Forecasting auditor’s going concern opinion using with hybrid robust machine learning model

Gradient Boosting Machine: A Survey

Boosting for high-dimensional two-class prediction

An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network

Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare Machine learning models for accurate liver disease prediction in healthcare

An Ensemble Classifiers for Improved Prediction of Native–Non-Native Protein–Protein Interaction

Efficient gradient boosting for prognostic biomarker discovery

Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry

Topological embedding and directional feature importance in ensemble classifiers for multi-class classification

Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

Advancing ecological networks: moving beyond binary classification to probabilistic interactions

Machine Learning Analysis of Molecular Dynamics Properties Influencing Drug Solubility

Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives

Boosted Decision Trees

XGBoost and Physical-Informed Neural Networks as Surrogate Models for Vapor-Liquid Equilibrium in PC-SAFT

Physics-based Machine Learning to Predict Hydration Free Energies for Small Molecules with a minimal number of descriptors: Interpretable and Accurate

Boosting Classifiers with Noisy Inference

Impacts of batch effects on the performance of machine learning classifiers across multiple studies

Vote-boosting ensembles

Enhanced version of AdaBoostM1 with J48 Tree learning method

An Efficient Machine Learning-Based Prediction Model for JAK2 Inhibitor pIC50

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Evaluation of classifier performance for multiclass phenotype discrimination in untargeted metabolomics

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning Identification of Active Sites in Graphite-Conjugated Catalysts