Kirk, P and Griffin, JE and Savage, RS and Ghahramani, Z and Wild, DL (2012) Bayesian correlated clustering to integrate multiple datasets. Bioinformatics, 28. pp. 3290-3297.Full text not available from this repository.
The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets.
|Additional Information:||PMCID: PMC3519452|
|Uncontrolled Keywords:||Bayes Theorem Chromatin Immunoprecipitation Cluster Analysis Gene Expression Gene Expression Profiling Genomics Models, Statistical Normal Distribution Oligonucleotide Array Sequence Analysis Protein Interaction Mapping Saccharomyces cerevisiae Systems Biology|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||02 Dec 2012 15:10|
|Last Modified:||30 Dec 2013 01:22|
Actions (login required)