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J Cell Mol Med ; 28(9): e18298, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38683133

RESUMO

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.


Assuntos
Redes Neurais de Computação , Humanos , Antineoplásicos/uso terapêutico , Antineoplásicos/farmacologia , Neoplasias/tratamento farmacológico , Aprendizado Profundo , Transdução de Sinais/efeitos dos fármacos , Biologia Computacional/métodos , Cisplatino/uso terapêutico , Cisplatino/farmacologia
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