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Robots and Minimal, Physics‐Informed Features: A Hybrid Framework for Enzyme Catalysis

Advanced Science, EarlyView.
Robotic experimentation and physics‐informed machine learning combine to predict enzyme substrate scope. With a handful of interpretable features derived from docking and quantum mechanics calculations, our model rivals descriptor‐heavy AI approaches and extrapolates to unseen substrates and enzyme classes.
Natalia Onishchenko +8 more
wiley +1 more source

A Dual‐Mode In Situ EV‐miRNA Profiling Platform for Machine‐Learning‐Assisted Gastric Cancer Liquid Biopsy

Advanced Science, EarlyView.
An in situ dual‐mode platform integrates fusogenic liposomes with localized autocatalytic DNA circuits for plasma‐derived extracellular vesicle microRNA analysis. Following membrane fusion, target miRNAs trigger feedback‐assisted LECHA amplification inside intact vesicles, generating fluorescence and electrochemical outputs. Machine learning integrates
Wenbin Li +15 more
wiley +1 more source

MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi‐Scale Information and Metric Learning Framework for scDNAm Data

Advanced Science, EarlyView.
MethyAnno enables robust and interpretable annotation of single‐cell DNA methylation data by integrating multi‐scale epigenetic information, bidirectional cross‐attention, and prototype‐based metric learning. The framework resolves rare and novel cell types across datasets while revealing cell‐type‐specific epigenetic signatures associated with disease
Yuhang Jia +4 more
wiley +1 more source

Soil microbial effects on tree seedling performance during forest secondary succession. [PDF]