Epilepsy affects millions of people worldwide and poses significant challenges for diagnosis and treatment. While traditional EEG analysis remains essential for seizure detection, it is often time-intensive and requires specialized expertise. To overcome these limitations, we proposed EpilepsyNet, an innovative deep learning architecture for automatic seizure detection. EpilepsyNet incorporates a self-calibration mechanism that enhances feature extraction by expanding the receptive field and enabling dynamic interactions across multiple EEG channels. Using neonatal EEG data, we validated EpilepsyNet’s effectiveness through five-fold cross-validation, demonstrating high classification accuracy and robustness across various performance metrics. Ablation experiments further underscored the important role of the self-calibrated convolution, as removing this component led to a notable decline in accuracy. These findings suggest that EpilepsyNet provides a scalable and reliable solution for seizure detection, with the potential to significantly improve clinical interventions by automating and optimizing the analysis of EEG data.

Transmembrane proteins are pivotal drug targets. Accurately predicting the impact of mutations on their stability can serve as an early indicator of alterations in protein function. Existing prediction methods based on protein crystal structures lack the capability for large-scale annotation of mutation effects on transmembrane proteins. In this study, we present a sequence-based protein language model-driven machine learning framework, MEMO-stab, to predict the effect of mutations on the stability of transmembrane proteins. MEMO-stab is an end-to-end binary classifier capable of rapidly and extensively annotating whether a mutation is destabilizing. We evaluated the integrative capability of MEMO-stab with eight diverse protein language models and conducted a performance comparison against alternative existing methods. We also designed case studies to further illustrate the advantage of our methods. MEMO-stab is the first sequence-based transmembrane protein-specific prediction tool capable of achieving reasonable results for destabilizing mutation prediction.

The study of transmembrane proteins (TMPs) and their binding activities holds significant importance in the pharmaceutical industry. Due to their physicochemical properties, known binding information regarding TMPs remains comparatively scarce and prevented researchers to dig information from known samples. However, research into general binding structure basis can circumvent this barrier and provide more mechanism insights, which we previously demonstrated its existence and named it as TMPs binding Domain. In this study, we try to discover the TMPs binding domain more precisely. Through atomic-level heterogenous graph convolutions, we significantly improved the classification performance of binding domains. This lays the algorithmic groundwork for utilizing binding domains in the study of TMPs binding activities and further boost the drug target research or new drug development.

Transmembrane proteins (TMPs) serve as drug targets for more than half of the drugs currently available in the market. However, it had not been clearly explained how they realize their drug effects through multiple complex molecules bindings actions. Research into TMPs bindings and corresponding structural basis will provide key information for drug research and new drug development. In this study, we defined the binding domain of TMPs according to the binding region investigation of multiple conjugate types. A 3D deep learning model was architected to discover the structural universality inside those domains. The experimental results proved such binding domains existing on the surface of TMPs, and they are structural specific distinguishing to the surface regions without any binding activities. This work provides a new theoretical basis for TMPs binding research and can greatly boost the development of the drug industry.