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Background: Human microbial communities play an important role in some physiological process of human beings. Nevertheless, the identification of microbe-disease associations through biological experiments is costly and time-consuming. Hence, the development of calculation models is meaningful to infer latent associations between microbes and diseases. Aims: In this manuscript, we aim to design a computational model based on the Graph Convolutional Neural Network with Multi-layer Attention mechanism, called GCNMA, to infer latent microbe-disease associations. objective: In this paper, we proposed a novel computational model based on the Graph Convolutional Neural Network with Multi-layer Attention mechanism, called GCNMA, to detect potential microbe-disease associations. Objective: This study aims to propose a novel computational model based on the Graph Convolutional Neural Network with Multi-layer Attention mechanism, called GCNMA, to detect potential microbe-disease associations. Method: In GCNMA, the known microbe-disease association network was first integrated with the microbe-microbe similarity network and the disease-disease similarity network into a heterogeneous network first. Subsequently, the graph convolutional neural network was implemented to extract embedding features of each layer for microbes and diseases respectively. Thereafter, these embedding features of each layer were fused together by adopting the multi-layer attention mechanism derived from the graph convolutional neural network, based on which, a bilinear decoder would be further utilized to infer possible associations between microbes and diseases. Result: Finally, to evaluate the predictive ability of GCNMA, intensive experiments were done and compared results with eight state-of-the-art methods which demonstrated that under the frameworks of both 2-fold cross-validations and 5-fold cross-validations, GCNMA can achieve satisfactory prediction performance based on different databases including HMDAD and Disbiome simultaneously. Moreover, case studies on three kinds of common diseases such as asthma, type 2 diabetes, and inflammatory bowel disease verified the effectiveness of GCNMA as well. Conclusion: GCNMA outperformed 8 state-of-the-art competitive methods based on the benchmarks of both HMDAD and Disbiome. other: no

With the continuous development and progress of machinery and industry equipment, human-machine interface has become an important operation in industry equipment and has been widely used in aviation, monitoring, traffic, special engineering vehicles, and a series of complex fields. Through the human-machine interface information system, information and data are provided for operators s. As the human-machine interface information data of the mechanical equipment system are numerous, complex, and changeable, operators often make operation mistakes, misread and misjudge, and do not give timely feedback, resulting in task failure or, in serious cases, major mechanical faults and accidents. Therefore, the human-machine interface data information is screened, and the information useful for the operator is directly obtained according to the target set by the operator, so as to effectively solve the complex and changeable data information in the information system. Human-machine interface uses electronic communication technology, computer network technology, and database technology to expand and update machinery and industrial equipment. Among them, nonlinear partial differential equations comprise an important branch of equation in mathematics. In this paper, according to the nonlinear partial differential equations, we research and analyze the information system of the human-machine interface design field and solve the system of the cognitive load, which is too large, such as cognitive mismatch problem in the working mode of operating personnel, by satisfying the needs of different users. The human-machine interface of mechanical industrial equipment uses visual optimization design and innovation.