the many-faced program of epithelial–mesenchymal transition: a system biology-based view

Clicks: 191
ID: 138772
2017
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #193 of 207 articles by views in international journal of heat and technology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 207 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
System biology uses a range of experimental and statistical methods to dissect complex processes that results from alterations in biological models. Given the complexity of the epithelial–mesenchymal transition (EMT) program, system biology represents a promising approach to understanding its fine molecular regulation by the interpretation of high-throughput datasets. Herein, we review recent contributions of system biology applied to the field of EMT physiology and illustrate the importance of these approaches to model biological networks that are perturbed during the transition. Together, these results allowed the definition of an EMT signature across different tumor types, the identification of dysregulated processes and new modules of regulation, making possible to reveal the EMT molecular visage underneath.
Reference Key
stefania2017frontiersthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;De Domenico Stefania;De Domenico Stefania;Daniele Vergara
Journal international journal of heat and technology
Year 2017
DOI
10.3389/fonc.2017.00274
URL
http://journal.frontiersin.org/article/10.3389/fonc.2017.00274/full
Keywords
network analysis system biology Oncology including cancer and carcinogens tumors regulatory networksneoplasms

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.