CCMB Seminar Series
Abstract:
One of the main challenges of systems biology is to cope with the overwhelming amount and diversity of omics data and a key problem is that of 'candidate gene prioritization' - i.e., selecting among a large list of candidate genes those that are most promising for further biological validation. We present ENDEAVOUR , a generic computational strategy to prioritize candidate genes based on their similarity (across multiple types of data, including sequence, expression, literature, annotation, etc.) to a set of genes already implicated in the process under scrutiny. We first validate the overall performance through a statistical cross-validation of 29 diseases and 3 biological pathways. Next, we validate a novel candidate for DiGeorge syndrome in a zebrafish model. Finally, we present an alternative machine learning strategy for gene prioritization using kernel methods. The key advantage of kernel methods in this context is that they provide an elegant framework for the fusion of data - by relying only on positive semi-definite kernel similarity matrices for the representation of heterogeneous data sources. Kernel-based novelty detection outperforms our previous method on our disease gene benchmark. Bio: My research interest falls in the broad field of bioinformatics and, more specifically into what I call Computational Systems Biomedicine, which is the application of computational methods in Systems Biology towards the understanding and modulation of developmental and pathological processes relevant to human health. The area of application in which I am currently most active is diagnosis and gene discovery in congenital disorders.
Dr. Moreau will be on campus Tuesday, July 29th. Individuals interested in meeting privately with him are encouraged to contact Louise Patterson at Louise_Patterson@Brown.edu or 863-3178.
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