Predicting cell cycle regulated genes by causal interactions.
The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory o...
Main Authors: | Frank Emmert-Streib, Matthias Dehmer |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2009-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC2723924?pdf=render |
Similar Items
-
Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries
by: Frank Emmert-Streib, et al.
Published: (2021-04-01) -
Named Entity Recognition and Relation Detection for Biomedical Information Extraction
by: Nadeesha Perera, et al.
Published: (2020-08-01) -
Utilizing Social Media Data for Psychoanalysis to Study Human Personality
by: Frank Emmert-Streib, et al.
Published: (2019-11-01) -
Exploring statistical and population aspects of network complexity.
by: Frank Emmert-Streib, et al.
Published: (2012-01-01) -
Influence of the time scale on the construction of financial networks.
by: Frank Emmert-Streib, et al.
Published: (2010-01-01)