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High-dimensional data acquired from biological experiments such as next generation sequencing are subject to a number of confounding effects.
“The Correlation between Relatives on the Supposition of Mendelian Inheritance”
RA Fisher · 1918
Earlier work this paper cites.
“The comparison of variability in populations having unequal means. An example of the analysis of covariance with multiple dependent and independent variates.”
B Day and RA Fisher · 1937
Earlier work this paper cites.
“Dimensionality Reduction by Learning an Invariant Mapping”
R. Hadsell, S. Chopra and Y. LeCun · 2006
Earlier work this paper cites.
“Adjusting batch effects in microarray expression data using empirical Bayes methods”
W. Johnson, Cheng Li and Ariel Rabinovic · 2007
Earlier work this paper cites.
“Correction for hidden confounders in the genetic analysis of gene expression”
Jennifer Listgarten, Carl Kadie, Eric. Schadt and David Heckerman · 2010
Earlier work this paper cites.
“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
Cited alongside, same era.
“Joint modeling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies”, 2012
N Fusi, O Stegle and N Lawrence · 2012
Cited alongside, same era.
“Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge”
Sara Mostafavi et al · 2013
Cited alongside, same era.
“Deep metric learning using Triplet network”
Elad Hoffer and Nir Ailon · 2014
Cited alongside, same era.
“Learning local feature descriptors with triplets and shallow convolutional neural networks”, The British Machine Vision Conference
Vassileios Balntas, Edgar Riba, Daniel Ponsa and Krystian Mikolajczyk · 2016
Later among the works it cites.
“Batch Effect Removal via Batch-Free Encoding”
Uri Shaham · 2018
Closest in time.
“Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA”
Nathan Wan et al · 2018
Closest in time.
Mihaela Pertea et al · 2018
Closest in time.
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