PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Predicting Binding of Transcriptional Regulators with a Two-way Latent Grouping Model
Samuel Kaski, Eerika Savia and Kai Puolamäki
In: 13th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2005), 25-29 Jun 2005, Detroit, Michigan.

Abstract

Binding of transcriptional regulators can be measured genome-wide to reveal regulatory networks. The measurements are noisy and expensive, however. We model existing binding data in order to predict binding for new factors or genes, assuming groups of genes and groups of transcription factors have similar binding patterns. We model the binding patterns using recent ideas from collaborative filtering and biclustering. A main difference from biclustering is that we compute a Bayesian prediction using all possible clusterings.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1200
Deposited By:Eerika Savia
Deposited On:24 November 2005