Name
Abstract | ||
|---|---|---|
Understanding how an individual genetic make-up influences their risk of diseases, is a problem of paramount importance. Although machine-learning techniques are unable to uncover the relationships between genotype and disease, we can still build the best biochemical model automatically with the help of methods that identify the DNA sequence variations in human populations that cause genetic diseases. In this paper, we study Petri net model that is bio chemically plausible to a certain degree, that it may reveal characteristics of the actual biochemical pathways in humans that can aid understanding of the disease. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BICTA.2010.5645278 | Bio-Inspired Computing: Theories and Applications |
Keywords | DocType | ISBN |
DNA,Petri nets,biochemistry,diseases,genetics,physiological models,DNA sequence variation,Petri net model,biochemical model,biochemical pathway,disease risk,genetic make-up,genotype-disease relationship,human population,nonlinear genetic disease,Petri net,automatic firing of transitions,bio chemical modeling | Conference | 978-1-4244-6437-1 |
Citations | PageRank | References |
1 | 0.35 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Persis Glory | 1 | 1 | 0.35 |
| N. Gnanamalar David | 2 | 2 | 2.74 |
| Emerald, J.D. | 3 | 1 | 0.35 |