Advances in molecular biological and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high throughput gene expression analysis, we are able to measure the output of the gene regulatory network of a cell. Here, we aim to review some datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. We discuss some aspects of clustering, ranging from distance measures to clustering algorithms. More advanced analysis aims to infer causal connections between genes directly. We discuss some approaches of reverse engineering of genetic networks and continuous linear model. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms and therapeutic targeting.
|Titolo:||Inference methods for gene regulatory networks|
|Data di pubblicazione:||27-ago-2009|
|Appare nelle tipologie:||8.1 PhD thesis|