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Found 7 papers, 3 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.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
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.
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.
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.
