James D. Malley

发表

Evaluating UMLS strings for natural language processing

Olivier Bodenreider, Alexa T. McCray, Allen C. Browne, 2001, AMIA.

SCORHE: A System for Automated Video-Based Assessment of Activity and Behavior for Mice Housed in a Home-Cage Environment

Tao Deng, James D. Malley, Ghadi Salem, 2013 .

Innovation is often unnerving: the door into summer

Jason H. Moore, James D. Malley, 2014, BioData Mining.

An integrated system for computer-aided diagnosis in CT colonography: work in progress

Ronald M. Summers, James D. Malley, Marek Franaszek, 2001, CARS.

POVMs and the Two Theorems of Naimark and Sz.-Nagy

James D. Malley, Anthony R. Fletcher, J. Malley, 2015, Axioms.

Statistical Learning for Biomedical Data: Error analysis and model validation

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

Three examples and several machines

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

Multinetwork classification scheme for detection of colonic polyps in CT colonography data sets

Ronald M. Summers, James D. Malley, Marek Franaszek, 2002, SPIE Medical Imaging.

O brave new world that has such machines in it

Jason H. Moore, James D. Malley, Karen G. Malley, 2014, BioData Mining.

Support vector machines committee classification method for computer-aided polyp detection in CT colonography1

Ronald M. Summers, James D. Malley, Marek Franaszek, 2005 .

Variance reduction for error estimation when classifying colon polyps from CT colonography

Ronald M. Summers, James D. Malley, Anna K. Jerebko, 2003, SPIE Medical Imaging.

Synthetic learning machines

James D. Malley, Hemant Ishwaran, H. Ishwaran, 2014, BioData Mining.

Feature selection for computer-aided polyp detection using genetic algorithms

Ronald M. Summers, James D. Malley, Anna K. Jerebko, 2003, SPIE Medical Imaging.

The behaviour of random forest permutation-based variable importance measures under predictor correlation

Carolin Strobl, James D. Malley, Andreas Ziegler, 2010, BMC Bioinformatics.

Automatic detection of endobronchial lesions using virtual bronchoscopy: comparison of two methods

Ronald M. Summers, James D. Malley, Lynne M. Pusanik, 1998, Medical Imaging.

Linearization of the Basic Model

James D. Malley, 1986 .

Detecting gene-gene interactions using a permutation-based random forest method

Jing Li, Jason H. Moore, James D. Malley, 2016, BioData Mining.

The disconnect between classical biostatistics and the biological data mining community

Jason H. Moore, James D. Malley, 2013, BioData Mining.

Variable Selection Method for the Identification of Epistatic Models

Qing Li, James D. Malley, Silke Szymczak, 2014, Pacific Symposium on Biocomputing.

Fractal analysis of virtual endoscopy reconstructions

Ronald M. Summers, James D. Malley, Lynne M. Pusanik, 1999, Medical Imaging.

Computer aided polyp detection in CT colonography using an ensemble of support vector machines

Ronald M. Summers, James D. Malley, Marek Franaszek, 2003, CARS.

Statistical Learning for Biomedical Data

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

COBRA: A combined regression strategy

James D. Malley, Gérard Biau, Benjamin Guedj, 2013, J. Multivar. Anal..

r2VIM: A new variable selection method for random forests in genome-wide association studies

James D. Malley, Abhijit Dasgupta, Dwight Stambolian, 2016, BioData Mining.

Supervising Random Forest Using Attribute Interaction Networks

Ting Hu, Jason H. Moore, James D. Malley, 2013, EvoBIO.

Joint Distributions and Quantum Nonlocal Models

James D. Malley, Anthony R. Fletcher, J. Malley, 2014, Axioms.

Statistical Learning for Biomedical Data: Random Forests – trees everywhere

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

Classical Probability and Quantum Outcomes

James D. Malley, J. Malley, 2014, Axioms.

Statistical Learning for Biomedical Data: Prologue

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

Multi network classification scheme for detection of colonic polyps in CT colonography data sets

Ronald M. Summers, James D. Malley, Marek Franaszek, 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

Predictor correlation impacts machine learning algorithms: implications for genomic studies

James D. Malley, Kristin K. Nicodemus, J. Malley, 2009, Bioinform..

The limits of p-values for biological data mining

Jason H. Moore, James D. Malley, Abhijit Dasgupta, 2013, BioData Mining.

Ensemble methods – let's take a vote

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

Risk estimation using probability machines

Jason H. Moore, James D. Malley, Abhijit Dasgupta, 2014, BioData Mining.

Statistical Learning for Biomedical Data: The landscape of learning machines

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

First complex, then simple

Jason H. Moore, James D. Malley, 2014, BioData Mining.

Quantum Statistical Inference

James D. Malley, John Hornstein, J. Malley, 1993 .

Using Multivariate Machine Learning Methods and Structural MRI to Classify Childhood Onset Schizophrenia and Healthy Controls

James D. Malley, Deanna Greenstein, Nitin Gogtay, 2012, Front. Psychiatry.

Statistical Applications of Jordan Algebras

James D. Malley, 1994 .

Statistical Learning for Biomedical Data: Preface

James D. Malley, Sinisa Pajevic, Karen G. Malley, 2011 .

Noncommuting observables and local realism

James D. Malley, Arthur Fine, 2005 .

A Comprehensive Method for Genome Scans

Daniel Q. Naiman, James D. Malley, Joan E. Bailey-Wilson, 2003, Human Heredity.

SCORHE: A novel and practical approach to video monitoring of laboratory mice housed in vivarium cage racks

James D. Malley, Marcial Garmendia-Cedillos, John U. Dennis, 2015, Behavior research methods.