Graph Attention Interaction Aggregation Network for Click-Through Rate Prediction.

Click-through rate prediction is a critical task for computational advertising and recommendation systems, where the key challenge is to model feature interactions between different feature domains. At present, the main click-through rate prediction models model feature interactions in an implicit way, which leads to poor interpretation of the model, and the interaction between each pair of features may introduce noise into the model, thus limiting the predictive ability of the model. In response to the above problems, this paper proposes a click-through rate prediction model (GAIAN) based on the graph attention interactive aggregation network, which explicitly obtains cross features on the graph structure. Our specific method is to design a feature interactive selection mechanism to select cross features that are beneficial to model prediction, reducing model noise and reducing the risk of model overfitting. On this basis, the bilinear interaction function is integrated into the aggregation strategy of the graph neural network, and the fine-grained intersection features are extracted in a flexible and explicit way, which makes graph neural networks more suitable for modeling feature interactions and enhances the interpretability of the model. Compared with several other state-of-the-art models on the Criteo and Avazu datasets, the experimental results show the superiority of the model.

A sensitive, homogeneous, and high-throughput assay for lysine-specific histone demethylases at the H3K4 site.

Histone methylation is a regulated feature of nucleosomes that can have an impact on gene expression. The methylation state of histone residues has also been found in recent years to be associated with various disorders. Tools for detecting methylation state changes are very useful for dissecting the function of these epigenetic marks. In this work, a sensitive homogeneous assay for histone demethylase activity at the H3K4 site has been developed in a time-resolved fluorescent resonance energy transfer assay format. The assay is based on the detection of the unmethylated H3 peptide by a fluorescent europium-chelate labeled monoclonal antibody binding specifically to the H3K4 site. The assay was validated for histone lysine-specific demethylase 1 and was demonstrated to be a suitable assay for inhibitor profiling and high-throughput screening.