Numerical prediction model for long- and short-term concentration of indoor volatile organic compounds from building materials.

Emission models of volatile organic compounds (VOCs) from individual indoor building materials have been developed and validated. However, multiple indoor building materials release VOCs simultaneously, and neither single building material nor multiple building material emission models can predict the entire release cycle of VOCs accurately. This study established a long- and short-term numerical prediction model for indoor VOC concentration. The model includes an attenuation coefficient θ. To describe the decay rate of the total VOC content, which is mainly influenced by time, and by designing experiments and testing in environmental warehouses under different seasonal conditions, the value of θ was first obtained. Then, after successfully plotting the emission curve of indoor pollutant concentration over time through numerical solution and using θ, the VOC content was corrected for various seasonal conditions. On the basis of this model, an exposure dose integration algorithm was proposed to evaluate the environmental health risks, as an application of this model. In comparison with previous research results and experimental data, this model has better predictive performance.

MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.

MOTIVATION: Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. RESULTS: MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. AVAILABILITY AND IMPLEMENTATION: https://github.com/1160300611/MCDHGN.