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Found 10 papers, 6 papers with code
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment
Several error analysis metrics such as the false positive rate (FPR), and the prediction uncertainty are evaluated, and the results are summarized by cancer type and drug mechanism of action (MoA) category.
Deep learning methods for drug response prediction in cancer: predominant and emerging trends
A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods.
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Prediction performance of three unimodal NNs which use GE are compared to assess the contribution of data augmentation methods.
Converting tabular data into images for deep learning with convolutional neural networks
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging.
Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening
Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).
A cross-study analysis of drug response prediction in cancer cell lines
To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow.
Learning Curves for Drug Response Prediction in Cancer Cell Lines
In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training sizes for two of the datasets, whereas the mNN performs better at the higher range of training sizes.
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response
Previous transfer learning studies for drug response prediction focused on building models that predict the response of tumor cells to a specific drug treatment.
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions.
