Algorithms for drug sensitivity prediction
Precision medicine entails the design of therapies that are matched for each individual
patient. Thus, predictive modeling of drug responses for specific patients constitutes a …
patient. Thus, predictive modeling of drug responses for specific patients constitutes a …
Probabilistic flight delay predictions using machine learning and applications to the flight-to-gate assignment problem
M Zoutendijk, M Mitici - Aerospace, 2021 - mdpi.com
The problem of flight delay prediction is approached most often by predicting a delay class
or value. However, the aviation industry can benefit greatly from probabilistic delay …
or value. However, the aviation industry can benefit greatly from probabilistic delay …
Functional random forest with applications in dose-response predictions
Drug sensitivity prediction for individual tumors is a significant challenge in personalized
medicine. Current modeling approaches consider prediction of a single metric of the drug …
medicine. Current modeling approaches consider prediction of a single metric of the drug …
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 …
Investigation of model stacking for drug sensitivity prediction
Background A significant problem in precision medicine is the prediction of drug sensitivity
for individual cancer cell lines. Predictive models such as Random Forests have shown …
for individual cancer cell lines. Predictive models such as Random Forests have shown …
Evaluating the consistency of large-scale pharmacogenomic studies
Recent years have seen an increase in the availability of pharmacogenomic databases such
as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia …
as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia …
Entropy-based weighting in random forest models
A weighting value is determined for each of a plurality of decision trees in a random forest
model hosted on a particular device, where the weighting is based on entropy of the …
model hosted on a particular device, where the weighting is based on entropy of the …
Application of transfer learning for cancer drug sensitivity prediction
Background In precision medicine, scarcity of suitable biological data often hinders the
design of an appropriate predictive model. In this regard, large scale pharmacogenomics …
design of an appropriate predictive model. In this regard, large scale pharmacogenomics …
Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm
J Lu, M Chen, Y Qin - BMC bioinformatics, 2021 - Springer
Background Predicting the drug response of the cancer diseases through the cellular
perturbation signatures under the action of specific compounds is very important in …
perturbation signatures under the action of specific compounds is very important in …
Tuning force field parameters of ionic liquids using machine learning techniques
The ability to estimate the force field parameters of materials is critical for calculating the
properties of materials using molecular dynamics (MD) simulations. The density functional …
properties of materials using molecular dynamics (MD) simulations. The density functional …