Spatio-temporal effects of built environment on running activity based on a random forest approach in nanjing, China.

Running activity is closely related to the urban built environment in terms of mental and physical health, and this relationship can change as a result of spatio-temporal changes. Most studies, however, do not account for this and assume a linear relationship exists between the built environment and running activity. This study, therefore, collected running data spanning 2019-2022, studied spatial distribution of four-year running activity, established built environment indicators, used a random forest approach to investigate the non-linear relationship between them, and evaluated spatio-temporal changes in the relationships over time.

TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides.

The accurate identification of anticancer peptides (ACPs) and antimicrobial peptides (AMPs) remains a computational challenge. We propose a tri-fusion neural network termed TriNet for the accurate prediction of both ACPs and AMPs. The framework first defines three kinds of features to capture the peptide information contained in serial fingerprints, sequence evolutions, and physicochemical properties, which are then fed into three parallel modules: a convolutional neural network module enhanced by channel attention, a bidirectional long short-term memory module, and an encoder module for training and final classification.