Cargando…
Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression
Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug resp...
Autores principales: | Qiu, Kexin, Lee, JoongHo, Kim, HanByeol, Yoon, Seokhyun, Kang, Keunsoo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korea Genome Organization
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042299/ https://www.ncbi.nlm.nih.gov/pubmed/33840174 http://dx.doi.org/10.5808/gi.20076 |
Ejemplares similares
-
MarkerCount: A stable, count-based cell type identifier for single-cell RNA-seq experiments
por: Kim, HanByeol, et al.
Publicado: (2022) -
Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape
por: Yoon, Seokhyun, et al.
Publicado: (2020) -
Role of lymphoid lineage cells aberrantly expressing alarmins S100A8/A9 in determining the severity of COVID-19
por: Lee, Joongho, et al.
Publicado: (2022) -
Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing
por: Lee, Joongho, et al.
Publicado: (2023) -
TraRECo: a greedy approach based de novo transcriptome assembler with read error correction using consensus matrix
por: Yoon, Seokhyun, et al.
Publicado: (2018)