Learning Causal Networks from Gene Expression Data - Ahsan, Nasir
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In this work we present a new model for identifying dependencies within a gene regulatory cycle. The model incorporates both probabilistic and temporal aspects, but is kept deliberately simple to make it amenable for learning from the gene expression data of microarray experiments. A key simplifying feature in our model is the use of a compression function for collapsing multiple causes of gene expression into a single cause. This allows us to introduce a learning algorithm which avoids the over-fitting tendencies of models with many parameters. We have validated the learning algorithm on…mehr

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Produktbeschreibung
In this work we present a new model for identifying dependencies within a gene regulatory cycle. The model incorporates both probabilistic and temporal aspects, but is kept deliberately simple to make it amenable for learning from the gene expression data of microarray experiments. A key simplifying feature in our model is the use of a compression function for collapsing multiple causes of gene expression into a single cause. This allows us to introduce a learning algorithm which avoids the over-fitting tendencies of models with many parameters. We have validated the learning algorithm on simulated data, and carried out experiments on real microarray data. In doing so, we have discovered novel, yet plausible, biological relationships.
Autorenporträt
Nasir Ahsan is currently a PhD student at the Australian center for Field Robotics working on adaptive ocean surveying. Prior to that he was a lecturer at NUST where he led a funded project on an Autonomous Air Vehicle. He received his MSc from UNSW in 2006 and his BSc from KFUPM in 2004.