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The algorithm is made up of three stages, called the training, scoring and fusion stages.

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Nevertheless, there is much evidence to suggest that functionally related genes often cause similar phenotypes ( 1–3 ).

To identify which genes are responsible for which phenotype, association studies and linkage analyses are often used, resulting in large lists of candidate genes.

This process leads to the generation of a large amount of genomic data and the creation and maintenance of corresponding databases.

However, converting genomic data into biological knowledge to identify genes involved in a particular process or disease remains a major challenge.

For example, with ontology-based data sources, genes are annotated with several terms and reciprocally one term can be associated to several genes.

The algorithm selects only the significant terms, the ones that are over-represented in the training sets compared to the complete genome.

However, most of the existing approaches mainly focus on the combination of few data sources.

For instance, the combining gene expression and protein interaction data method proposed by Ma ( 16 ).

The most commonly used data sources are text-mining data, gene expression data and sequence information.

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