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Jessica C Mar

Showing results (1-10 of 62) with videos related to

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Biophysical Reviews|January 9, 2019
The rise of the distributions: why non-normality is important for understanding the transcriptome and beyondJessica C Mar
BMC Bioinformatics|June 20, 2018
Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression dataShuonan 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 cancersRaymund Bueno, Jessica C Mar
Plos Computational Biology|December 31, 2009
Decomposition of gene expression state space trajectoriesJessica 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 typesTianyuan Lu, Jessica C Mar
Genome Biology|December 16, 2006
Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samplesJessica C Mar, Renee Rubio, John Quackenbush
Bioinformatics (Oxford, England)|February 19, 2011
Defining an informativeness metric for clustering gene expression dataJessica C Mar, Christine A Wells, John Quackenbush
BMC Bioinformatics|December 22, 2019
Investigating skewness to understand gene expression heterogeneity in large patient cohortsBenjamin 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 survivalDaniel 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 violationJessica C Mar, Timothy J Harlow, Mark A Ragan
Pageof 7

Showing results (1-10 of 62) with videos related to

Sort By:
Pageof 7
Biophysical Reviews|January 9, 2019
The rise of the distributions: why non-normality is important for understanding the transcriptome and beyondJessica C Mar
BMC Bioinformatics|June 20, 2018
Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression dataShuonan 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 cancersRaymund Bueno, Jessica C Mar
Plos Computational Biology|December 31, 2009
Decomposition of gene expression state space trajectoriesJessica 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 typesTianyuan Lu, Jessica C Mar
Genome Biology|December 16, 2006
Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samplesJessica C Mar, Renee Rubio, John Quackenbush
Bioinformatics (Oxford, England)|February 19, 2011
Defining an informativeness metric for clustering gene expression dataJessica C Mar, Christine A Wells, John Quackenbush
BMC Bioinformatics|December 22, 2019
Investigating skewness to understand gene expression heterogeneity in large patient cohortsBenjamin 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 survivalDaniel 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 violationJessica C Mar, Timothy J Harlow, Mark A Ragan
Pageof 7