📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer. (2023)

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1016/j.biopha.2023.114256

PubMed Identifier: 36652730

Publication URI: http://europepmc.org/abstract/MED/36652730

Type: Journal Article/Review

Volume: 159

Parent Publication: Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

ISSN: 0753-3322