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Found 12 papers, 6 papers with code
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis
To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e. g., predictive accuracy across datasets) and relative performance (e. g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability.
Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches
Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein.
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data
In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models.
Entropy-Reinforced Planning with Large Language Models for Drug Discovery
Finally, to further illustrate the capabilities of ERP, we tested our algorithm on three code generation benchmarks and outperformed the current state-of-the-art approach as well.
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.
Blending Imitation and Reinforcement Learning for Robust Policy Improvement
Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner's performance surpasses that of the oracles in a specific state.
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
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.
Cost-Effective Online Contextual Model Selection
In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection.
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.
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.
