Graph convolution networks model identifies and quantifies gene and cancer specific transcriptome signatures of cancer driver events
The STAMP models provide a learning framework that successfully identifies and quantifies driver events' signal strength, showing utility in portraying the molecular landscape of tumors based on transcriptomics. Importantly, STAMP manifested the ability to improve targeted therapy selection and hence can contribute to better treatment.
Authors
- Ranel Loutati (ORCID: https://orcid.org/0009-0008-9012-9540)
- Shai Rosenberg (ORCID: https://orcid.org/0000-0001-6309-6933)
- Tom Hope (ORCID: https://orcid.org/0000-0003-1232-3635)
- Adar Yaacov (ORCID: https://orcid.org/0000-0002-6106-2307)
- Natan Lishinsky
- Jakob Landau (ORCID: https://orcid.org/0000-0002-7672-7084)
- Gil Ben Cohen (ORCID: https://orcid.org/0000-0001-8857-6124)
- Yishai Ben Zvi
- Aron Popovzter
Institutions
- Hebrew University of Jerusalem (IL)
- Hadassah Medical Center (IL)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109491
- Citations
- 4
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.94