Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. Here, the authors present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data...
Enregistré dans:
Auteurs principaux: | JungHo Kong, Heetak Lee, Donghyo Kim, Seong Kyu Han, Doyeon Ha, Kunyoo Shin, Sanguk Kim |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2020
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/bc2d4c908ac14d54ad3b9ed62cdef10f |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Semiconducting polymer nanoparticles for photothermal ablation of colorectal cancer organoids
par: Bryce McCarthy, et autres
Publié: (2021) -
A bladder cancer patient-derived xenograft displays aggressive growth dynamics in vivo and in organoid culture
par: Elise Y. Cai, et autres
Publié: (2021) -
A new murine esophageal organoid culture method and organoid-based model of esophageal squamous cell neoplasia
par: Biyun Zheng, et autres
Publié: (2021) -
Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening
par: Minsuh Kim, et autres
Publié: (2019) -
Pharmacodynamic Studies of Fluorescent Diamond Carriers of Doxorubicin in Liver Cancer Cells and Colorectal Cancer Organoids
par: Firestein R, et autres
Publié: (2021)