Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer-Reference-Cited by-同舟云学术

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer

Published:2020-11-20 Issue:1 Volume:18 Page:016001
ISSN:1478-3975
Container-title:Physical Biology
language:
Short-container-title:Phys. Biol.

Author:

Johnson Kaitlyn E,Howard Grant R,Morgan Daylin^ORCID,Brenner Eric A^ORCID,Gardner Andrea L,Durrett Russell E,Mo William,Al’Khafaji Aziz,Sontag Eduardo D,Jarrett Angela M,Yankeelov Thomas E,Brock Amy

Funder

National Science Foundation Graduate Research Fellowship

National Cancer Institute

Cancer Prevention and Research Institute of Texas

National Science Foundation

NIH iMAT Program

Publisher

IOP Publishing

Subject

Cell Biology,Molecular Biology,Structural Biology,Biophysics

Link

https://iopscience.iop.org/article/10.1088/1478-3975/abb09c/pdf

Reference58 articles.

1. Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity

2. Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer

3. Automated high-dimensional flow cytometric data analysis

4. Quantitative single-cell RNA-seq with unique molecular identifiers

5. Chromatin and Single-Cell RNA-Seq Profiling Reveal Dynamic Signaling and Metabolic Transitions during Human Spermatogonial Stem Cell Development

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