Meta-analysis by Information Content (MAIC)


MAIC encodes a simple heuristic to meta-analyse ranked and unranked lists of related named entities. You, the user, decide what is meant by "related": give MAIC a set of input lists, for example experimental results naming genes implicated in a given biological process, and MAIC will iteratively weigh them against each other to learn which lists perform best. The best-performing lists are those that find entities (genes, in our example) that are also found on the other input lists. For a full description of MAIC, see our paper: Li B et al. Nature Communications 11:164(2020)

While we are offline you can still download the code here [github.com/baillielab/maic](https://github.com/baillielab/maic)

Meta-analysis by Information Content (MAIC): submit job

Instructions

Host factors required for influenza virus replication

In this example analysis, we have aggregated experimental results from a variety of sources relating to host genes involved in influenza virus replication. Data from published sources, together with *unpublished data from Bo Li, JK Baillie and Nir Hacohen.*

Click here to browse the results of this example MAIC analysis.

Code

Code to run the MAIC algorithm can be downloaded from github.com/baillielab/maic.