Abstract
Network theory can give a useful overview of how a biological system works. But to make testable predictions, we need the details.
Keywords
Affiliated Institutions
Related Publications
Next-Generation Machine Learning for Biological Networks
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological resea...
A framework for mesencephalic dopamine systems based on predictive Hebbian learning
We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations...
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists
Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging an...
Interactome data and databases: different types of protein interaction
Abstract In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological highâthroughput experimental methods and bioinforma...
A global pathway crosstalk network
Abstract Motivation: Given the complex nature of biological systems, pathways often need to function in a coordinated fashion in order to produce appropriate physiological respo...
Publication Info
- Year
- 2003
- Type
- article
- Volume
- 301
- Issue
- 5641
- Pages
- 1864-1865
- Citations
- 212
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1126/science.1089118