EssSubgraph improves performance and generalizability of mammalian essential gene prediction with large networks
Our results show that EssSubgraph effectively combines networks and omics data for accurate essential gene identification while maintaining computational efficiency. The source code and datasets used in this study are freely available at https://github.com/wenmm/EssSubgraph.
Authors
- Andrew Nelson (ORCID: https://orcid.org/0000-0002-4071-3502)
- Haimei Wen
- Keriayn N. Smith (ORCID: https://orcid.org/0000-0002-4351-2765)
- Susan Carpenter (ORCID: https://orcid.org/0000-0002-6876-6948)
- Tian Hong (ORCID: https://orcid.org/0000-0002-8212-7050)
- Karen McGinnis (ORCID: https://orcid.org/0000-0002-9564-8146)
Institutions
- Florida State University (US)
- University of North Carolina at Chapel Hill (US)
- The University of Texas at Dallas (US)
- University of California, Santa Cruz (US)
- Cornell University (US)
Publication Details
- Journal
- GigaScience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/gigascience/giaf136
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00