Abstract:
In line with recent advances in neural drug design and sensitivity prediction, we introduce a novel architecture for the
interpretable prediction of anticancer compound sensitivity utilizing a multimodal attention-based convolutional encoder. Our approach
is based on three primary foundations: prior knowledge of intracellular interactions from protein-protein interaction networks, gene
expression profiles of tumors, and the structure of chemicals as a SMILES sequence. With R2 = 0.86 and RMSE = 0.89, our multiscale convolutional attention-based encoder significantly outperforms a baseline model trained on Morgan fingerprints, a set of
SMILES-based encoders, and the previously reported state-of-the-art for multimodal drug sensitivity prediction. Talk about the
Ensemble Convolution Neural Network Model: A Novel Regression-Based Approach (ECNN-NRNN) to Drug Sensitivity Analysis
Using Multiple Pharma Omics Data Sets and Addressing Heterogeneity in Feature Selection for Sub-Pharma Omics Parameters.
Because some pharmacogenomics data is available online and should be made publicly available, it is essential to address drug
sensitivity prediction and drug identification and design. Outline how the performance in sensitivity prediction can be improved using
conventional methods, and provide an experimental evaluation. Implemented a New Model for Drug Sensitivity Identification
Using Ensemble Convolution Neural Networks (ECNN-NRNN) and Various Pharmacogenomic Data Sets This paper
analyzes the amount of chemicals in cancer cell lines, a multi-regression assessment method should be used.
