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GCRNN: Group-Constrained Convolutional Recurrent Neural Network

IEEE transaction on neural networks and learning systems, 2018-10, Vol.29 (10), p.4709-4718

2018

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Correction: Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform

PloS one, 2018-06, Vol.13 (6), p.e0200224-e0200224

2018

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A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas

BMC genomics, 2018-11, Vol.19 (1), p.841-841, Article 841

2018

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MTBR: Multi-Target Boosting for Regression

IEEE transactions on knowledge and data engineering, 2021-02, Vol.33 (2), p.626-636

2021

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Matched Forest: supervised learning for high-dimensional matched case–control studies

Bioinformatics, 2020-03, Vol.36 (5), p.1570-1576

2020

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CRAFTER: A Tree-Ensemble Clustering Algorithm for Static Datasets with Mixed Attributes and High Dimensionality

IEEE transactions on knowledge and data engineering, 2018-09, Vol.30 (9), p.1686-1696

2018

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Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform

PloS one, 2018-04, Vol.13 (4), p.e0196556-e0196556

2018

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Discovering the Nature of Variation in Nonlinear Profile Data

Technometrics, 2016-08, Vol.58 (3), p.371-382

2016

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Feature selection for noisy variation patterns using kernel principal component analysis

Knowledge-based systems, 2014-12, Vol.72, p.37-47

2014

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Using metarules to organize and group discovered association rules

Data mining and knowledge discovery, 2007-06, Vol.14 (3), p.409-431

2007

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Tangent Hyperplane Kernel Principal Component Analysis for Denoising

IEEE transaction on neural networks and learning systems, 2012-04, Vol.23 (4), p.644-656

2012

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Interpretable regularized class association rules algorithm for classification in a categorical data space

Information sciences, 2019-05, Vol.483, p.313-331

2019

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Factory cycle-time prediction with a data-mining approach

IEEE transactions on semiconductor manufacturing, 2006-05, Vol.19 (2), p.252-258

2006

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Supervised learning for change-point detection

International journal of production research, 2006-07, Vol.44 (14), p.2853-2868

2006

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Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins

Bioinformatics, 2009-08, Vol.25 (15), p.1905-1914

2009

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Automated feature extraction from profiles with application to a batch fermentation process

Applied statistics, 2012-03, Vol.61 (2), p.327-344

2012

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Partitioned partial least squares regression with application to a batch fermentation process

Journal of chemometrics, 2011-04, Vol.25 (4), p.159-168

2011

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A Novel Feature Selection Methodology for Automated Inspection Systems

IEEE transactions on pattern analysis and machine intelligence, 2009-07, Vol.31 (7), p.1338-1344

2009

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Using Experimental Design to Find Effective Parameter Settings for Heuristics

Journal of heuristics, 2001-01, Vol.7 (1), p.77

2001

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Robustness of the EWMA Control Chart to Non-Normality

Journal of quality technology, 1999-07, Vol.31 (3), p.309-316

1999

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Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: missing value imputation using temporal datasets

Molecular bioSystems, 2011-01, Vol.7 (4), p.1093-1104

2011

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Assignable Causes and Autocorrelation: Control Charts for Observations or Residuals?

Journal of quality technology, 2002-04, Vol.34 (2), p.165-170

Runger, George C.

2002

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A Markov Chain Model for the Multivariate Exponentially Weighted Moving Averages Control Chart

Journal of the American Statistical Association, 1996-12, Vol.91 (436), p.1701-1706

1996

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Designing a Multivariate EWMA Control Chart

Journal of quality technology, 1997-01, Vol.29 (1), p.8-15

1997

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A statistical framework for multiparameter analysis at the single-cell level

Molecular bioSystems, 2012-03, Vol.8 (3), p.804-817

2012

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GCRNN: Group-Constrained Convolutional Recurrent Neural Network

Correction: Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform

A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas

MTBR: Multi-Target Boosting for Regression

Matched Forest: supervised learning for high-dimensional matched case–control studies

CRAFTER: A Tree-Ensemble Clustering Algorithm for Static Datasets with Mixed Attributes and High Dimensionality

Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform

Discovering the Nature of Variation in Nonlinear Profile Data

Feature selection for noisy variation patterns using kernel principal component analysis

Using metarules to organize and group discovered association rules

Tangent Hyperplane Kernel Principal Component Analysis for Denoising

Interpretable regularized class association rules algorithm for classification in a categorical data space

Factory cycle-time prediction with a data-mining approach

Supervised learning for change-point detection

Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins

Automated feature extraction from profiles with application to a batch fermentation process

Partitioned partial least squares regression with application to a batch fermentation process

A Novel Feature Selection Methodology for Automated Inspection Systems

Using Experimental Design to Find Effective Parameter Settings for Heuristics

Robustness of the EWMA Control Chart to Non-Normality

Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: missing value imputation using temporal datasets

Assignable Causes and Autocorrelation: Control Charts for Observations or Residuals?

A Markov Chain Model for the Multivariate Exponentially Weighted Moving Averages Control Chart

Designing a Multivariate EWMA Control Chart

A statistical framework for multiparameter analysis at the single-cell level

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