Abstract
The human genome is a complex system characterized by gene interactions and nonlinear behaviors. Complex systems cannot be viewed as the aggregate of their isolated pieces but must be studied as an integrated whole. Microarray technologies offer the opportunity to see the entire biological system as it existed at one moment in time. It is tempting to try to analyze the entire microarray at once to immediately discover the pattern being sought, for example, the pattern of a breast cancer. However, such an analysis would be a mistake because microarrays provide massively parallel information, the analysis of which is a nondeterministic polynomial time (NP)-hard problem. Current statistical methods are not sufficiently powerful to solve this NP-hard problem. The best approach to microarray analysis is to begin with a small number of the elements in the microarray known to be a pattern and ask questions of the other elements in the microarray; i.e., perform instantaneous scientific experiments regarding whether each of the other elements in the microarray are related to the known pattern.
| Original language | English |
|---|---|
| Pages (from-to) | 349-357 |
| Number of pages | 9 |
| Journal | Molecular Diagnosis |
| Volume | 5 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2000 |
| Externally published | Yes |
Keywords
- Artificial neural networks
- Clustering
- Microarray
- Nondeterministic polynomial time-hard problem
- Pattern discovery
- Pattern recognition
- Predictive medicine
- Principal components analysis
- Self-organizing maps