New consensus ranking heuristics to rank big biological data

Ranking biological data is a difficult task for various reasons. Hence several ranking methods have been proposed, but none of them has been deployed on systems currently used by the scientific community. This is why a good solution is, given a set of rankings, to find a consensus ranking that reflect their common points while not putting too large an emphasis on elements that are classified as “good” by only one or a few rankings.

In computer science, we know that this problem is hard to solve when we have to find the consensus of more than 3 rankings and gets even harder when the rankings are getting bigger. Thus, we are interested in designing efficient algorithms to solve the problem and quick heuristics that can give a good quality approximate results on big data. We will also explore mathematical properties of the problem to have a better understanding of its mechanics and to accelerate computational calculations.

Faculty Supervisor:

Sylvie Hamel

Student:

Partner:

Université Paris Saclay

Discipline:

Computer science

Sector:

University:

Université de Montréal

Program:

Globalink Research Award