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Sander W. Keemink et al.
CURRENT OPINION IN NEUROBIOLOGY (2019)
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IEEE Journal of Biomedical and Health Informatics (2019)
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Jianguo Chen et al.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS (2017)
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Chong Peng et al.
NEUROCOMPUTING (2017)
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Thierry Bouwmans et al.
COMPUTER SCIENCE REVIEW (2017)
Demixed principal component analysis of neural population data
Dmitry Kobak et al.
ELIFE (2016)
A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data Environment
Jianguo Chen et al.
IEEE ACCESS (2016)
DeMix: deconvolution for mixed cancer transcriptomes using raw measured data
Jaeil Ahn et al.
BIOINFORMATICS (2013)
Context-dependent computation by recurrent dynamics in prefrontal cortex
Valerio Mante et al.
NATURE (2013)
Neural Correlates of a Decision Variable Before Learning to Perform a Match/Non-Match Task
Xue-Lian Qi et al.
JOURNAL OF NEUROSCIENCE (2012)
A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers
Sahand N. Negahban et al.
STATISTICAL SCIENCE (2012)
Changes in Prefrontal Neuronal Activity after Learning to Perform a Spatial Working Memory Task
Xue-Lian Qi et al.
CEREBRAL CORTEX (2011)
Stimulus Selectivity in Dorsal and Ventral Prefrontal Cortex after Training in Working Memory Tasks
Travis Meyer et al.
JOURNAL OF NEUROSCIENCE (2011)
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Emmanuel J. Candes et al.
JOURNAL OF THE ACM (2011)
Power and sample size estimation in microarray studies
Wei-Jiun Lin et al.
BMC BIOINFORMATICS (2010)
Principal component analysis
Herve Abdi et al.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS (2010)
A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
Daniela M. Witten et al.
BIOSTATISTICS (2009)
A proximal decomposition method for solving convex variational inverse problems
Patrick L. Combettes et al.
INVERSE PROBLEMS (2008)
Sparse principal component analysis via regularized low rank matrix approximation
Haipeng Shen et al.
JOURNAL OF MULTIVARIATE ANALYSIS (2008)
Neural correlates, computation and behavioural impact of decision confidence
Adam Kepecs et al.
NATURE (2008)
What is principal component analysis?
Markus Ringner
NATURE BIOTECHNOLOGY (2008)
A direct formulation for sparse PCA using semidefinite programming
Alexandre d'Aspremont et al.
SIAM REVIEW (2007)
Representation of spatial goals in rat orbitofrontal cortex
Claudia E. Feierstein et al.
NEURON (2006)
Sparse principal component analysis
Hui Zou et al.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2006)
Regularization and variable selection via the elastic net
H Zou et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2005)
Optimal sample size for multiple testing:: The case of gene expression microarrays
P Müller et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2004)
Timing and neural encoding of somatosensory parametric working memory in macaque prefrontal cortex
CD Brody et al.
CEREBRAL CORTEX (2003)
A modified principal component technique based on the LASSO
IT Jolliffe et al.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2003)
Power and sample size for DNA microarray studies
MLT Lee et al.
STATISTICS IN MEDICINE (2002)
Determination of minimum sample size and discriminatory expression patterns in microarray data
DH Hwang et al.
BIOINFORMATICS (2002)