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Numerous studies have demonstrated that microbes play a vital role in human health, making the identification of potential microbe-drug associations critical for drug discovery and clinical treatment. In this manuscript, we proposed a novel prediction model named GTDEKAN by integrating an aware Transformer network with a Dual Cross-Attention (DCA) module (including a Channel Cross-Attention and a Spatial Cross-Attention) and an Enhanced Kolmogorov-Arnold Network (EKAN) to infer potential microbe-drug associations.

A dataset incldes 841 nodes. This dataset includes 841 nodes in a mobile social network, used to simulate the process of users being interconnected and influencing each other within the mobile social network. Each row of data consists of two numbers, representing the current location of the node. In the process of information dissemination, each user is a node, influenced by their neighboring nodes and also influencing those neighbors in return.

Nasal Cytology, or Rhinology, is the subfield of otolaryngology, focused on the microscope observation of samples of the nasal mucosa, aimed to recognize cells of different types, to spot and diagnose ongoing pathologies. Such methodology can claim good accuracy in diagnosing rhinitis and infections, being very cheap and accessible without any instrument more complex than a microscope, even optical ones.

Scatterplots provide a visual representation of bivariate data (or 2D embeddings of multivariate data) that allows for effective analyses of data dependencies, clusters, trends, and outliers. Unfortunately, classical scatterplots suffer from scalability issues, since growing data sizes eventually lead to overplotting and visual clutter on a screen with a fixed resolution, which hinders the data analysis process. We propose an algorithm that compensates for irregular sample distributions by a smooth transformation of the scatterplot's visual domain.