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 Data from published sources, together with *unpublished data from Bo Li, JK Baillie and Nir Hacohen.*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.

Host genes implicated in SARS-CoV2 infection

In this example analysis, we have aggregated results from a variety of sources relating to host genes implicated in SARS-CoV2 infection.

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

Click here to browse the published paper of this example MAIC analysis.

Code

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