Interpretable Recurrent Neural Networks for Modeling Temporal Dynamics of Acquired Drug Resistance in Targeted Therapy

dc.contributor.authorPushpalatha V.
dc.contributor.authorThanvi Kuttaiah
dc.contributor.authorKamaleshwar T.
dc.contributor.authorAnat Jaslin Jini A.
dc.contributor.authorJakkapu Nagalakshmidevi
dc.contributor.authorSusmitha V.
dc.contributor.authorSathyaraju S.
dc.contributor.authorJayasudha R.
dc.contributor.authorDeepa A.R
dc.contributor.authorVengatesh T
dc.date.accessioned2026-05-26T09:11:34Z
dc.date.available2026-05-26T09:11:34Z
dc.date.issued2026-05-26
dc.description.abstractAcquired 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
dc.identifier.urihttp://192.168.75.5:4000/handle/123456789/21421
dc.titleInterpretable Recurrent Neural Networks for Modeling Temporal Dynamics of Acquired Drug Resistance in Targeted Therapy
dc.typeLearning Object
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