Identifying key genes in cancer networks using persistent homology
Abstract Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the...
Saved in:
Main Authors: | Rodrigo Henrique Ramos, Yago Augusto Bardelotte, Cynthia de Oliveira Lage Ferreira, Adenilso Simao |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-87265-4 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Persistent Homology Combined with Machine Learning for Social Network Activity Analysis
by: Zhijian Zhang, et al.
Published: (2024-12-01) -
Persistent Topological Laplacians—A Survey
by: Xiaoqi Wei, et al.
Published: (2025-01-01) -
Topology Unveiled: A New Horizon for Economic and Financial Modeling
by: Yicheng Wei, et al.
Published: (2025-01-01) -
Adams and Steenrod operators in dihedral homology
by: Y. Ch. Gouda
Published: (1998-01-01) -
Quantum distance approximation for persistence diagrams
by: Bernardo Ameneyro, et al.
Published: (2025-01-01)