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Jessica C Mar

Showing results (11-20 of 62) with videos related to

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Plos Genetics|July 18, 2020
Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networksAbhirami Ratnakumar, Nils Weinhold, Jessica C Mar, et al.
Mutation Research|March 4, 2015
Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated allelesWenge Li, R Brent Calder, Jessica C Mar, et al.
Plant Science : an International Journal of Experimental Plant Biology|October 26, 2013
Not to be suppressed? Rethinking the host response at a root-parasite interfaceDerek B Goto, Hikota Miyazawa, Jessica C Mar, et al.
Gigascience|January 24, 2023
scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing dataMalindrie Dharmaratne, Ameya S Kulkarni, Atefeh Taherian Fard, et al.
Plos One|October 25, 2011
attract: A method for identifying core pathways that define cellular phenotypesJessica C Mar, Nicholas A Matigian, John Quackenbush, et al.
British Journal of Cancer|March 2, 2019
A novel approach to modelling transcriptional heterogeneity identifies the oncogene candidate CBX2 in invasive breast carcinomaDaniel G Piqué, Cristina Montagna, John M Greally, et al.
Alzheimer'S & Dementia (New York, N. Y.)|January 1, 2020
Estrogen activates Alzheimer's disease genesAbhirami Ratnakumar, Samuel E Zimmerman, Bryen A Jordan, et al.
NPJ Systems Biology and Applications|July 21, 2017
Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networksAtefeh Taherian Fard, Sriganesh Srihari, Jessica C Mar, et al.
Peerj|June 1, 2017
<i>pathVar:</i> a new method for pathway-based interpretation of gene expression variabilityLaurence de Torrente, Samuel Zimmerman, Deanne Taylor, et al.
BMC Bioinformatics|December 29, 2020
The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic dataLaurence de Torrenté, Samuel Zimmerman, Masako Suzuki, et al.
Pageof 7

Showing results (11-20 of 62) with videos related to

Sort By:
Pageof 7
Plos Genetics|July 18, 2020
Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networksAbhirami Ratnakumar, Nils Weinhold, Jessica C Mar, et al.
Mutation Research|March 4, 2015
Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated allelesWenge Li, R Brent Calder, Jessica C Mar, et al.
Plant Science : an International Journal of Experimental Plant Biology|October 26, 2013
Not to be suppressed? Rethinking the host response at a root-parasite interfaceDerek B Goto, Hikota Miyazawa, Jessica C Mar, et al.
Gigascience|January 24, 2023
scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing dataMalindrie Dharmaratne, Ameya S Kulkarni, Atefeh Taherian Fard, et al.
Plos One|October 25, 2011
attract: A method for identifying core pathways that define cellular phenotypesJessica C Mar, Nicholas A Matigian, John Quackenbush, et al.
British Journal of Cancer|March 2, 2019
A novel approach to modelling transcriptional heterogeneity identifies the oncogene candidate CBX2 in invasive breast carcinomaDaniel G Piqué, Cristina Montagna, John M Greally, et al.
Alzheimer'S & Dementia (New York, N. Y.)|January 1, 2020
Estrogen activates Alzheimer's disease genesAbhirami Ratnakumar, Samuel E Zimmerman, Bryen A Jordan, et al.
NPJ Systems Biology and Applications|July 21, 2017
Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networksAtefeh Taherian Fard, Sriganesh Srihari, Jessica C Mar, et al.
Peerj|June 1, 2017
<i>pathVar:</i> a new method for pathway-based interpretation of gene expression variabilityLaurence de Torrente, Samuel Zimmerman, Deanne Taylor, et al.
BMC Bioinformatics|December 29, 2020
The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic dataLaurence de Torrenté, Samuel Zimmerman, Masako Suzuki, et al.
Pageof 7