Synthetic cells (SCs), developed through bottom-up synthetic biology, hold great potential for biomedical applications, with the promise of replacing malfunctioning natural cells and treating diseases with spatiotemporal control. Currently, most SC s...
3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for ...
Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoe...
Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer-related OXS. Hence, constructing a TP53-NRF2 integrated OXS signature of pan-cancer ...
Machine learning has proven useful in analyzing complex biological data and has greatly influenced the course of research in structural biology and precision medicine. Deep neural network models oftentimes fail to predict the structure of complex pro...
Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead ...
DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or ins...