Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
0301 basic medicine
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
drug sensitivity prediction; computational systems biology; machine learning; drug discovery; multiscale; multimodal; attention; CNN; RNN; explainability; interpretability; molecular networks; molecular fingerprints; GDSC; SMILES; gene expression; drug sensitivity; anticancer compounds; IC50; EC50; lead discovery; personalized medicine; precision medicine
GDSC
precision medicine
anticancer compounds,
Antineoplastic Agents
Machine Learning (stat.ML)
IC50
Quantitative Biology - Quantitative Methods
RNN
Machine Learning (cs.LG)
drug discovery
03 medical and health sciences
Deep Learning
Statistics - Machine Learning
explainability
computational systems biology
Humans
molecular networks
drug sensitivity
lead discovery
Quantitative Methods (q-bio.QM)
drug sensitivity prediction
molecular fingerprints
0303 health sciences
multimodal
deep learning
personalized medicine
SMILES
anticancer compounds
attention
EC50
Artificial Intelligence (cs.AI)
machine learning
multiscale
Drug Design
FOS: Biological sciences
gene expression
Neural Networks, Computer
interpretability
Algorithms
CNN
DOI:
10.1021/acs.molpharmaceut.9b00520
Publication Date:
2019-10-16T21:54:10Z
AUTHORS (6)
ABSTRACT
ISSN:1543-8384<br/>ISSN:1543-8392<br/>Molecular Pharmaceutics, 16 (12)<br/>
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CITATIONS (113)
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