A network-biology perspective of microRNA function and dysfunction in cancer

By C. Bracken, H. Scott, Gregory J. Goodall. 2016

635 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Casey S. Greene
2. G. Way
3. Jianguo Xia
4. Jinbo Xu
5. Le Chang
6. Guangyan Zhou
7. Othman Soufan
8. Yvonne Tay

In Molecular Biology

1. Casey S. Greene
2. Yifan Peng
3. Brett K. Beaulieu-Jones
4. Guangyan Zhou
5. G. Way
6. Amr M. Alexandari
7. Othman Soufan
8. Le Chang

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Prediction of microRNAs (miRNAs) in the genome as well as miRNA targets is of great interest, as they are critical components of gene regulatory networks and are often conserved across great evolutionary distance [182,183].”
• Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types (2019, background): “In particular, increasing evidence suggests the existence of cooperation and crosstalk between miRNAs and TFs, mainly to buffer gene expression and/or adjust signalling [12].”
• Noncoding RNA:RNA Regulatory Networks in Cancer (2018, background): “expression of miRNAs, which could exhibit either tumor suppressive or oncogenic effects in a context-dependent manner [13,14].”
• miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology (2020, background): “From TFs to networks. miRNAs and TFs can cooperate to tune gene expression, or mutually regulate each other in feedback loops (4,47).”