Ovchinnikova, Katja; Born, Jannis; Chouvardas, Panagiotis; Rapsomaniki, Marianna; Kruithof-de Julio, Marianna (2024). Overcoming limitations in current measures of drug response may enable AI-driven precision oncology. NPJ precision oncology, 8(95) Springer Nature 10.1038/s41698-024-00583-0
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Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models - they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Urologie 04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Urologie 04 Faculty of Medicine > Department of Dermatology, Urology, Rheumatology, Nephrology, Osteoporosis (DURN) > Clinic of Urology 04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) |
UniBE Contributor: |
Ovchinnikova, Katja, Chouvardas, Panagiotis, Kruithof-de Julio, Marianna |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2397-768X |
Publisher: |
Springer Nature |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
25 Apr 2024 12:22 |
Last Modified: |
25 Apr 2024 12:31 |
Publisher DOI: |
10.1038/s41698-024-00583-0 |
PubMed ID: |
38658785 |
BORIS DOI: |
10.48350/196220 |
URI: |
https://boris.unibe.ch/id/eprint/196220 |
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