2025-26

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http://192.168.75.5:4000/handle/123456789/21417

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Browsing 2025-26 by Title

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    A Multi-Model Deep Learning Approach for Early Assessment of Drug-Induced Toxicitie
    (2026-05-26) Kampa Belliappa; Anat Jaslin Jini A; E. Ramalakshmi
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    Interpretable Recurrent Neural Networks for Modeling Temporal Dynamics of Acquired Drug Resistance in Targeted Therapy
    (2026-05-26) Pushpalatha V.; Thanvi Kuttaiah; Kamaleshwar T.; Anat Jaslin Jini A.; Jakkapu Nagalakshmidevi; Susmitha V.; Sathyaraju S.; Jayasudha R.; Deepa A.R; Vengatesh T
    Acquired drug resistance remains a fundamental obstacle in targeted cancer therapy, often arising from complex temporal evolutionary dynamics within tumor cell populations. Traditional pharmacokinetic-pharmacodynamic (PKPD) models struggle to capture nonlinear, long-term sequential dependencies, while standard recurrent neural networks (RNNs) suffer from black-box opacity. In this paper, we propose IRNN-DR (Interpretable RNN for Drug Resistance), a novel architecture combining Long Short-Term Memory (LSTM) units with an attention-based temporal relevance mask and a learnable dynamical system backbone. Using longitudinal single- cell resistance data from EGFR-mutant non-small cell lung cancer (NSCLC) cell lines treated with osimertinib, we demonstrate that IRNN-DR achieves high predictive accuracy (AUC-ROC = 0.94) while providing explicit interpretations of resistance-driving time points and genetic pathways. Our contribution includes a temporal Shapley value module and a resistance trajectory clustering method. Results indicate that IRNN-DR can identify early "critical windows" of resistance emergence, with potential clinical utility for adaptive therapy scheduling
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