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https://www.selleckchem.com/TGF-beta.html com/liushaomin/MitosisDetection.Non-small cell lung cancer (NSCLC) caused by the mutation of epidermal growth factor receptor (EGFR) is a major cause of death worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been developed against the EGFR. These TKIs produce promising results at initial stage of therapy, but the efficacy becomes limited due to the emergence of drug resistance in most cases after about an year, due to a secondary point mutation. In this work, we investigated the drug resistance mechanism due to the EGFR mutations. We performed molecular dynamics (MD) simulation for EGFR-drug interactions complexes. Euclidean distance and binding free energy are used for drug resistance analysis and drug-protein interactions visualization. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. Overall, we have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of lung cancer drug resistance at atomic level.Reinforcement learning is a powerful tool for developing personalized treatment regimens from healthcare data. Yet training reinforcement learning agents through direct interactions with patients is often impractical for ethical reasons. One solution is to train reinforcement learning agents using an 'environment model,' which is learned from retrospective patient data and can simulate realistic patient trajectories. In this study, we propose transitional variational autoencoders (tVAE), a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. Unlike other models, the tVAE requires few distributional assumptions and benefits from identical training and testing architectures. This model produces more realistic patient trajectories than state-of-the-art sequential decision-making models and generative neural networks

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