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Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

Journal of Chemical Information and Modeling, 2009-09, Vol.49 (9), p.2077-2081

2009

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Mid‐ to Far‐Infrared Anisotropic Dielectric Function of HfS2 and HfSe2

Advanced optical materials, 2022-12, Vol.10 (23), p.n/a

2022

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How wrong can we get? A review of machine learning approaches and error bars

Combinatorial chemistry & high throughput screening, 2009-06, Vol.12 (5), p.453

2009

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A Probabilistic Approach to Classifying Metabolic Stability

Journal of chemical information and computer sciences, 2008-04, Vol.48 (4), p.785-796

2008

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Mid‐ to Far‐Infrared Anisotropic Dielectric Function of HfS 2 and HfSe 2

Advanced optical materials, 2022-12, Vol.10 (23)

2022

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Mid‐ to Far‐Infrared Anisotropic Dielectric Function of HfS 2 and HfSe 2

Advanced optical materials, 2022-09, Vol.10 (23)

2022

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Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces

IEEE transactions on pattern analysis and machine intelligence, 2003-05, Vol.25 (5), p.623-628

2003

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Constructing boosting algorithms from SVMs: an application to one-class classification

IEEE transactions on pattern analysis and machine intelligence, 2002-09, Vol.24 (9), p.1184-1199

2002

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Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

Journal of chemical information and modeling, 2007-03, Vol.47 (2), p.407-424

2007

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Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Journal of chemical information and modeling, 2005-03, Vol.45 (2), p.249-253

2005

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Bias-Correction of Regression Models: A Case Study on hERG Inhibition

Journal of Chemical Information and Modeling, 2009-06, Vol.49 (6), p.1486-1496

2009

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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Journal of computer-aided molecular design, 2007-09, Vol.21 (9), p.485-498

2007

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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Journal of computer-aided molecular design, 2007-12, Vol.21 (12), p.651-664

2007

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Machine Learning Models for Lipophilicity and Their Domain of Applicability

Molecular pharmaceutics, 2007-07, Vol.4 (4), p.524-538

2007

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Predicting Lipophilicity of Drug-Discovery Molecules using Gaussian Process Models

ChemMedChem, 2007-09, Vol.2 (9), p.1265-1267

2007

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Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

Artificial Neural Networks — ICANN 2001, 2001, p.331-338

2001

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An introduction to kernel-based learning algorithms

IEEE transactions on neural networks, 2001-03, Vol.12 (2), p.181-201

2001

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A kernel view of the dimensionality reduction of manifolds

Proceedings of the twenty-first international conference on Machine learning, 2004, p.47

2004

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Predicting Error Bars for QSAR Models

AIP conference proceedings, 2007, Vol.940 (1), p.158-167

2007

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Input space versus feature space in kernel-based methods

IEEE transactions on neural networks, 1999, Vol.10 (5), p.1000-1017

1999

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Regularized Principal Manifolds

Computational Learning Theory, 1999, p.214-229

1999

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Robust Ensemble Learning for Data Mining

Knowledge Discovery and Data Mining. Current Issues and New Applications, 2000, p.341-344

2000

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Kernels, Pre-Images and Optimization

Empirical Inference, 2013, p.245-259

2013

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Support Vector Machines

Handbook of Computational Statistics, p.883-926

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A genetically encoded FRET lactate sensor and its use to detect the Warburg effect in single cancer cells

PloS one, 2013-02, Vol.8 (2), p.e57712-e57712

2013

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Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

Mid‐ to Far‐Infrared Anisotropic Dielectric Function of HfS2 and HfSe2

How wrong can we get? A review of machine learning approaches and error bars

A Probabilistic Approach to Classifying Metabolic Stability

Mid‐ to Far‐Infrared Anisotropic Dielectric Function of HfS 2 and HfSe 2

Mid‐ to Far‐Infrared Anisotropic Dielectric Function of HfS 2 and HfSe 2

Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces

Constructing boosting algorithms from SVMs: an application to one-class classification

Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Bias-Correction of Regression Models: A Case Study on hERG Inhibition

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Machine Learning Models for Lipophilicity and Their Domain of Applicability

Predicting Lipophilicity of Drug-Discovery Molecules using Gaussian Process Models

Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

An introduction to kernel-based learning algorithms

A kernel view of the dimensionality reduction of manifolds

Predicting Error Bars for QSAR Models

Input space versus feature space in kernel-based methods

Regularized Principal Manifolds

Robust Ensemble Learning for Data Mining

Kernels, Pre-Images and Optimization

Support Vector Machines

A genetically encoded FRET lactate sensor and its use to detect the Warburg effect in single cancer cells

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