ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer.

ABSTRACT: Breast cancer remains a leading cause of mortality among women worldwide, with drug resistance driven by transcription factors and mutations posing significant challenges. To address this, we present ResisenseNet, a predictive model for drug sensitivity and resistance. ResisenseNet integrates transcription factor expression, genomic markers, drugs, and molecular descriptors, employing a hybrid architecture of 1D-CNN + LSTM and DNN to effectively learn long-range and temporal patterns from amino acid sequences and transcription factor data. The model demonstrated exceptional predictive accuracy, achieving a validation accuracy of 0.9794 and a loss value of 0.042. Comprehensive validation included comparisons with state-of-the-art models and ablation studies, confirming the robustness of the

SUBMITTER: Karampuri A 

PROVIDER: S-EPMC11471817 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature