This work proposes a unified framework to leveragebiological information in network propagation-based gene prior-itization algorithms. Preliminary results on breast cancer datashow significant improvements over state-of-the-art baselines,such as the prioritization of genes that are not identified aspotential candidates by interactome-based algorithms, but thatappear to be involved in/or potentially related to breast cancer,according to a functional analysis based on recent literature.
2019, 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019, Pages 1-8
Biological Random Walks: Integrating heterogeneous data in disease gene prioritization (04b Atto di convegno in volume)
Gentili M., Martini L., Petti M., Farina L., Becchetti L.
Gruppo di ricerca: Algorithms and Data Science