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)
The MAIC online submission form is currently experiencing problems due to high demand - please check back in a day or two (posted on 8th January 2020)
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 Data from published sources, together with *unpublished data from Bo Li, JK Baillie and Nir Hacohen.*
Code to run the MAIC algorithm can be downloaded from github.com/baillielab/maic.