A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data
Danielle Maeser(University of Minnesota), R. Stephanie Huang(Sun Yat-sen University), Yingbo Huang(Kunming University of Science and Technology), Weijie Zhang(University of Minnesota)
Cited by 22
Related Papers
oncoPredict: an R package for predicting <i>in vivo</i> or cancer patient drug response and biomarkers from cell line screening data
|Briefings in Bioinformatics|2021|1.8k
The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare
|BioMedInformatics|2024|36
SlGH3.15, a member of the GH3 gene family, regulates lateral root development and gravitropism response by modulating auxin homeostasis in tomato
|Plant Science|2023|27
Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors
|Cancer Research|2024|21
Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling
|Proceedings of the National Academy of Sciences|2023|17