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Expert Review of Molecular Diagnostics
|
October 6, 2023
An update on current and novel molecular diagnostics for the diagnosis of invasive fungal infections
Jeffrey D Jenks, P Lewis White, Sarah E Kidd, et al.
Journal of Clinical Microbiology
|
April 17, 2020
Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting
April Aralar, Yixu Yuan, Kevin Chen, et al.
Iscience
|
January 25, 2021
Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity
Kevin Chen, Kivilcim Ozturk, Ryne L Contreras, et al.
Scientific Reports
|
February 9, 2017
Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling
Daniel Ortiz Velez, Hannah Mack, Julietta Jupe, et al.
Plos One
|
October 3, 2014
Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants
Pornpat Athamanolap, Vishwa Parekh, Stephanie I Fraley, et al.
Cancer Research
|
July 5, 2012
NAC1 is an actin-binding protein that is essential for effective cytokinesis in cancer cells
Kai Lee Yap, Stephanie I Fraley, Michelle M Thiaville, et al.
Integrative Biology : Quantitative Biosciences From Nano to Macro
|
June 29, 2019
3D collagen architecture regulates cell adhesion through degradability, thereby controlling metabolic and oxidative stress
Daniel O Velez, Sural K Ranamukhaarachchi, Aditya Kumar, et al.
BMC Bioinformatics
|
May 10, 2024
Machine learning based DNA melt curve profiling enables automated novel genotype detection
Aaron Boussina, Lennart Langouche, Augustine C Obirieze, et al.
Nucleic Acids Research
|
October 18, 2015
Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples
Stephanie I Fraley, Justin Hardick, Billie J Masek, et al.
Biorxiv : the Preprint Server for Biology
|
July 10, 2023
Divergent iron-regulatory states contribute to heterogeneity in breast cancer aggressiveness
William D Leineweber, Maya Z Rowell, Sural Ranamukhaarachchi, et al.
Page
of 5
Search research articles
Search
Showing results (21-30 of 45) with videos related to
Sort By:
Page
of 5
Expert Review of Molecular Diagnostics
|
October 6, 2023
An update on current and novel molecular diagnostics for the diagnosis of invasive fungal infections
Jeffrey D Jenks, P Lewis White, Sarah E Kidd, et al.
Journal of Clinical Microbiology
|
April 17, 2020
Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting
April Aralar, Yixu Yuan, Kevin Chen, et al.
Iscience
|
January 25, 2021
Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity
Kevin Chen, Kivilcim Ozturk, Ryne L Contreras, et al.
Scientific Reports
|
February 9, 2017
Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling
Daniel Ortiz Velez, Hannah Mack, Julietta Jupe, et al.
Plos One
|
October 3, 2014
Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants
Pornpat Athamanolap, Vishwa Parekh, Stephanie I Fraley, et al.
Cancer Research
|
July 5, 2012
NAC1 is an actin-binding protein that is essential for effective cytokinesis in cancer cells
Kai Lee Yap, Stephanie I Fraley, Michelle M Thiaville, et al.
Integrative Biology : Quantitative Biosciences From Nano to Macro
|
June 29, 2019
3D collagen architecture regulates cell adhesion through degradability, thereby controlling metabolic and oxidative stress
Daniel O Velez, Sural K Ranamukhaarachchi, Aditya Kumar, et al.
BMC Bioinformatics
|
May 10, 2024
Machine learning based DNA melt curve profiling enables automated novel genotype detection
Aaron Boussina, Lennart Langouche, Augustine C Obirieze, et al.
Nucleic Acids Research
|
October 18, 2015
Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples
Stephanie I Fraley, Justin Hardick, Billie J Masek, et al.
Biorxiv : the Preprint Server for Biology
|
July 10, 2023
Divergent iron-regulatory states contribute to heterogeneity in breast cancer aggressiveness
William D Leineweber, Maya Z Rowell, Sural Ranamukhaarachchi, et al.
Page
of 5