Abstract
With the continuous improvement of computer performance and related technology, the combination of molecular communication networks (MCN) and artificial intelligence (AI) can find the prediction to analyze target methods that are molecular diagnosis (MD) and more efficient. To overcome this issue, researchers have suggested the novel paradigm of the MD that combines nanoparticles-medicine (NPs-M) with tools from synthetic biology to provide re-engineering of biological computing devices. Here, in this work, the prediction model of the target is established by combining the drug calculation model of MCN and deep neural network (DNN) including classification performance for prediction. We developed a numerical analysis model to refer to drug target scalability (DTS), on a genome-wide scale based on protein to protein interaction (PPI) such as in-term of cardiac-disease. Finally, the prediction of the strength and direction of binding affinity between drugs and targets is achieved. Furthermore, to expand the application of the model, with a combination of genetic databases, the micro-processes related to genes are displayed. Then, to combine medicine, a multi-layer network is established based on the effect on/off mechanism. We establish a systematic MCN evaluation model that reveals the basis of MD of medicine and serves for screening, reorientation, drug development, and other fields of medical healthcare industries (MHI). The engineering finding demonstrates that the integration of phenotypic, chemical index in the NPs-M and PPI cannot only achieve scalability but also finds new applications for an existing drug.
Published Version
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