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Abstract Details
Activity Number:
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223
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #301345 |
Title:
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Resolving the Structure of Interactions with Hierarchical Agglomerative Clustering
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Author(s):
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Yongjin Park*+
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Companies:
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The Johns Hopkins University
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Address:
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Clark 217, Johns Hopkins Univers, Baltimore, MD, 21218,
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Keywords:
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biological networks ;
model comparison ;
clustering ;
link prediction ;
systems biology
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Abstract:
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Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. A new algorithm, Hierarchical Agglomerative Clustering (HAC), is developed for fast clustering of heterogeneous interaction networks. This algorithm uses maximum likelihood to drive the inference of a hierarchical stochastic block model for network structure. Bayesian model selection provides a principled method both for identifying the major top-level groups and for collapsing the fine-structure of the bottom-level groups within a network. Model scores are additive over independent edge types, providing a direct route for simultaneous analysis of multiple biological interactions. In addition to inferring network structure, this algorithm generates link predictions that with cross-validation provide a quantitative assessment of performance for real-world examples. When applied to genome-scale data sets representing several organisms and interaction types, HAC provides the overall best performance in link prediction when compared with other clustering methods and with model-free graph diffusion kernels.
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Authors who are presenting talks have a * after their name.
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