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Biophysical Reviews
|
January 9, 2019
The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond
Jessica C Mar
BMC Bioinformatics
|
June 20, 2018
Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data
Shuonan Chen, Jessica C Mar
Methods (San Diego, Calif.)
|
July 30, 2017
Changes in gene expression variability reveal a stable synthetic lethal interaction network in BRCA2-ovarian cancers
Raymund Bueno, Jessica C Mar
Plos Computational Biology
|
December 31, 2009
Decomposition of gene expression state space trajectories
Jessica C Mar, John Quackenbush
Biology of Sex Differences
|
November 6, 2020
Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types
Tianyuan Lu, Jessica C Mar
Genome Biology
|
December 16, 2006
Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples
Jessica C Mar, Renee Rubio, John Quackenbush
Bioinformatics (Oxford, England)
|
February 19, 2011
Defining an informativeness metric for clustering gene expression data
Jessica C Mar, Christine A Wells, John Quackenbush
BMC Bioinformatics
|
December 22, 2019
Investigating skewness to understand gene expression heterogeneity in large patient cohorts
Benjamin V Church, Henry T Williams, Jessica C Mar
BMC Cancer
|
September 7, 2020
Identification of a novel subgroup of endometrial cancer patients with loss of thyroid hormone receptor beta expression and improved survival
Daniel G Piqué, John M Greally, Jessica C Mar
BMC Evolutionary Biology
|
January 29, 2005
Bayesian and maximum likelihood phylogenetic analyses of protein sequence data under relative branch-length differences and model violation
Jessica C Mar, Timothy J Harlow, Mark A Ragan
Page
of 7
Search research articles
Search
Showing results (1-10 of 62) with videos related to
Sort By:
Page
of 7
Biophysical Reviews
|
January 9, 2019
The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond
Jessica C Mar
BMC Bioinformatics
|
June 20, 2018
Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data
Shuonan Chen, Jessica C Mar
Methods (San Diego, Calif.)
|
July 30, 2017
Changes in gene expression variability reveal a stable synthetic lethal interaction network in BRCA2-ovarian cancers
Raymund Bueno, Jessica C Mar
Plos Computational Biology
|
December 31, 2009
Decomposition of gene expression state space trajectories
Jessica C Mar, John Quackenbush
Biology of Sex Differences
|
November 6, 2020
Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types
Tianyuan Lu, Jessica C Mar
Genome Biology
|
December 16, 2006
Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples
Jessica C Mar, Renee Rubio, John Quackenbush
Bioinformatics (Oxford, England)
|
February 19, 2011
Defining an informativeness metric for clustering gene expression data
Jessica C Mar, Christine A Wells, John Quackenbush
BMC Bioinformatics
|
December 22, 2019
Investigating skewness to understand gene expression heterogeneity in large patient cohorts
Benjamin V Church, Henry T Williams, Jessica C Mar
BMC Cancer
|
September 7, 2020
Identification of a novel subgroup of endometrial cancer patients with loss of thyroid hormone receptor beta expression and improved survival
Daniel G Piqué, John M Greally, Jessica C Mar
BMC Evolutionary Biology
|
January 29, 2005
Bayesian and maximum likelihood phylogenetic analyses of protein sequence data under relative branch-length differences and model violation
Jessica C Mar, Timothy J Harlow, Mark A Ragan
Page
of 7