The aim of biological data ranking is to help users face with huge amounts of data and choose between alternative pieces of information. This is particularly important in the context of querying biological data, where very simple queries can return thousands of answers. Ranking biological data is a difficult task: data may be associated with various degrees of confidence; data are not independent of each other’s; various ranking criteria can be considered (the most well-known data ranked first, or the freshest, or the most surprising, etc.). Consensus Ranking techniques are very promising in this context. However, such approaches are intrinsically complex, a plethora of approximations and heuristics have thus been designed, making the choice of the approach to follow very difficult to make.
The aim of the QualiBioConsensus project is to carefully study the problem of consensus ranking techniques for biological data both practically and fundamentally by:
- performing a comparative study of rank aggregation algorithms
- providing new results on the complexity of the problem and
- designing new efficient algorithms or heuristics
- assessing the biological significance of the results obtained by consensus ranking techniques.