[HTML][HTML] Machine learning approaches to drug response prediction: challenges and recent progress
Cancer is a leading cause of death worldwide. Identifying the best treatment using
computational models to personalize drug response prediction holds great promise to …
computational models to personalize drug response prediction holds great promise to …
[HTML][HTML] Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens
Cancer drug development has been riddled with high attrition rates, in part, due to poor
reproducibility of preclinical models for drug discovery. Poor experimental design and lack of …
reproducibility of preclinical models for drug discovery. Poor experimental design and lack of …
Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions
Like physics in the 19th century, biology and molecular biology in particular, has been
fertilized and enhanced like few other scientific fields, by the incorporation of mathematical …
fertilized and enhanced like few other scientific fields, by the incorporation of mathematical …
[HTML][HTML] An improved anticancer drug-response prediction based on an ensemble method integrating matrix completion and ridge regression
C Liu, D Wei, J Xiang, F Ren, L Huang, J Lang… - … Therapy-Nucleic Acids, 2020 - cell.com
In this study, we proposed an ensemble learning method, simultaneously integrating a low-
rank matrix completion model and a ridge regression model to predict anticancer drug …
rank matrix completion model and a ridge regression model to predict anticancer drug …
[HTML][HTML] Ensemble transfer learning for the prediction of anti-cancer drug response
Transfer learning, which transfers patterns learned on a source dataset to a related target
dataset for constructing prediction models, has been shown effective in many applications …
dataset for constructing prediction models, has been shown effective in many applications …
[HTML][HTML] Network-based drug sensitivity prediction
Background Drug sensitivity prediction and drug responsive biomarker selection on high-
throughput genomic data is a critical step in drug discovery. Many computational methods …
throughput genomic data is a critical step in drug discovery. Many computational methods …
[HTML][HTML] Assessment of modelling strategies for drug response prediction in cell lines and xenografts
Data from several large high-throughput drug response screens have become available to
the scientific community recently. Although many efforts have been made to use this …
the scientific community recently. Although many efforts have been made to use this …
Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity
L Deng, Y Cai, W Zhang, W Yang… - Journal of Chemical …, 2020 - ACS Publications
To efficiently save cost and reduce risk in drug research and development, there is a
pressing demand to develop in silico methods to predict drug sensitivity to cancer cells. With …
pressing demand to develop in silico methods to predict drug sensitivity to cancer cells. With …
[HTML][HTML] Feature selection strategies for drug sensitivity prediction
K Koras, D Juraeva, J Kreis, J Mazur, E Staub… - Scientific reports, 2020 - nature.com
Drug sensitivity prediction constitutes one of the main challenges in personalized medicine.
Critically, the sensitivity of cancer cells to treatment depends on an unknown subset of a …
Critically, the sensitivity of cancer cells to treatment depends on an unknown subset of a …
[PDF][PDF] Identifying States of Collateral Sensitivity during the Evolution of Therapeutic Resistance in Ewing's Sarcoma
Advances in the treatment of Ewing's sarcoma (EWS) are desperately needed, particularly in
the case of metastatic disease. A deeper understanding of collateral sensitivity, where the …
the case of metastatic disease. A deeper understanding of collateral sensitivity, where the …