Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets.

ABSTRACT: In Genetics, gene sets are grouped in collections concerning their biological function. This often leads to high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation of their biological meaning. In Data Mining, it is often argued that techniques to reduce the dimensionality of data could increase the maneuverability and consequently the interpretability of large data. In the past years, moreover, we witnessed an increasing consciousness of the importance of understanding data and interpretable models in the machine learning and bioinformatics communities. On the one hand, there exist techniques aiming to aggregate overlapping gene sets to create larger pathways. While these methods could partly solve the large size of the collections' p

SUBMITTER: Balestra C 

PROVIDER: S-EPMC9997904 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature