From knockouts to networks: establishing direct cause-effect relationships through graph analysis |
| |
Authors: | Pinna Andrea Soranzo Nicola de la Fuente Alberto |
| |
Affiliation: | Center for Advanced Studies, Research and Development (CRS4) Bioinformatica, Pula, Italy. |
| |
Abstract: | BackgroundReverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.ResultsWe propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.ConclusionsOur algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference. |
| |
Keywords: | |
本文献已被 PubMed 等数据库收录! |
|