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The Ensemble Bridge Algorithm: A New Modeling Tool for Drug Discovery Problems

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1006/game.1999.0738openalex-snapshot

Adaptive Game Playing Using Multiplicative Weights

inferredcites →https://doi.org/10.1006/inco.1995.1136openalex-snapshot

Boosting a Weak Learning Algorithm by Majority

inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-snapshot

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

inferredcites →https://doi.org/10.1016/s0167-9473(01)00065-2openalex-snapshot

Stochastic gradient boosting

inferredcites →https://doi.org/10.1017/cbo9780511812651openalex-snapshot

Pattern Recognition and Neural Networks

inferredcites →https://doi.org/10.1021/ci0500379openalex-snapshot

Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling

inferredcites →https://doi.org/10.1021/ci050518sopenalex-snapshot

Classification Tree Models for the Prediction of Blood−Brain Barrier Passage of Drugs

inferredcites →https://doi.org/10.1021/ci050519kopenalex-snapshot

Assessing Different Classification Methods for Virtual Screening

inferredcites →https://doi.org/10.1021/ci050520jopenalex-snapshot

Random Forest Prediction of Mutagenicity from Empirical Physicochemical Descriptors

inferredcites →https://doi.org/10.1021/ci060164kopenalex-snapshot

Random Forest Models To Predict Aqueous Solubility

inferredcites →https://doi.org/10.1021/ci600289vopenalex-snapshot

Virtual Screening of Chinese Herbs with Random Forest

inferredcites →https://doi.org/10.1021/ci600332jopenalex-snapshot

Contemporary QSAR Classifiers Compared

inferredcites →https://doi.org/10.1021/jm040835aopenalex-snapshot

Derivation and Validation of Toxicophores for Mutagenicity Prediction

inferredcites →https://doi.org/10.1021/jm040854kopenalex-snapshot

Docking and 3-D QSAR Studies on Indolyl Aryl Sulfones. Binding Mode Exploration at the HIV-1 Reverse Transcriptase Non-Nucleoside Binding Site and Design of Highly Active N-(2-Hydroxyethyl)carboxamide and N-(2-Hydroxyethyl)carbohydrazide Derivatives

inferredcites →https://doi.org/10.1021/jm0497141openalex-snapshot

A Comparison of Methods for Modeling Quantitative Structure−Activity Relationships

inferredcites →https://doi.org/10.1023/a:1007607513941openalex-snapshot

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1023/a:1022648800760openalex-snapshot

The Strength of Weak Learnability

inferredcites →https://doi.org/10.1109/34.88569openalex-snapshot

Optimal partitioning for classification and regression trees

inferredcites →https://doi.org/10.1145/1968.1972openalex-snapshot

A theory of the learnable

inferredcites →https://doi.org/10.1145/3749740.3749746openalex-snapshot

A Theory of the Learnable

inferredcites →https://doi.org/10.1162/neco.1997.9.7.1545openalex-snapshot

Shape Quantization and Recognition with Randomized Trees

inferredcites →https://doi.org/10.1177/001316446002000104openalex-snapshot

A Coefficient of Agreement for Nominal Scales

inferredcites →https://doi.org/10.1214/009053604000000058openalex-snapshot

Generalization bounds for averaged classifiers

inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot

Greedy function approximation: A gradient boosting machine.

inferredcites →https://doi.org/10.1214/aos/1016218223openalex-snapshot

Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)

inferredcites →https://doi.org/10.1214/aos/1024691352openalex-snapshot

Boosting the margin: a new explanation for the effectiveness of voting methods

inferredcites →https://doi.org/10.18637/jss.v017.i02openalex-snapshot

ada: AnRPackage for Stochastic Boosting

inferredcites →https://doi.org/10.18637/jss.v028.i05openalex-snapshot

Building Predictive Models in R Using the caret Package

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9783527645121.ch7openalex-snapshot

Structural‐Similarity‐Based Approaches for the Development of Clustering and QSPR/QSAR Models in Chemical Databases

inferred← citeshttps://doi.org/10.1002/cem.2518openalex-snapshot

Quantitative analysis of tea using ytterbium‐based internal standard near‐infrared spectroscopy coupled with boosting least‐squares support vector regression

inferred← citeshttps://doi.org/10.1007/978-3-642-21827-9_41openalex-snapshot

Prediction of Drug Activity Using Molecular Fragments-Based Representation and RFE Support Vector Machine Algorithm

inferred← citeshttps://doi.org/10.1007/s11094-013-0922-5openalex-snapshot

Collective recognition strategy for estimating hepatoprotector activity of various chemical compounds in increasing liver repair potential

inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00419openalex-snapshot

xMaP—An Interpretable Alignment-Free Four-Dimensional Quantitative Structure–Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles

inferred← citeshttps://doi.org/10.1021/ci100429uopenalex-snapshot

A Robust Boosting Regression Tree with Applications in Quantitative Structure−Activity Relationship Studies of Organic Compounds

inferred← citeshttps://doi.org/10.1021/ci2000083openalex-snapshot

Comparison of Combinatorial Clustering Methods on Pharmacological Data Sets Represented by Machine Learning-Selected Real Molecular Descriptors

inferred← citeshttps://doi.org/10.1021/ci200028nopenalex-snapshot

Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers

inferred← citeshttps://doi.org/10.1039/c0an00345jopenalex-snapshot

Detecting influential observations by cluster analysis and Monte Carlo cross-validation

inferred← citeshttps://doi.org/10.1109/compsac.2014.65openalex-snapshot

A Semi-supervised Approach to Software Defect Prediction

inferred← citeshttps://doi.org/10.1145/2020390.2020405openalex-snapshot

An iterative semi-supervised approach to software fault prediction

inferred← citeshttps://doi.org/10.1198/jcgs.2010.09076openalex-snapshot

On Adaptive Regularization Methods in Boosting

inferred← citeshttps://doi.org/10.3390/challe17030024openalex-snapshot

Leveraging Behavioral Tipping Points to Improve Sustainability Outcomes in Consumer Electronics

inferred← citeshttps://doi.org/10.48550/arxiv.1706.01109openalex-snapshot

InfiniteBoost: building infinite ensembles with gradient descent

inferred← citeshttps://doi.org/10.5555/2025816.2025857openalex-snapshot

Prediction of drug activity using molecular fragments-based representation and RFE support vector machine algorithm

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