AI Medical Compendium Journal:
Nature communications

Showing 61 to 70 of 854 articles

Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem.

Nature communications
A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in...

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.

Nature communications
RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale w...

Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials.

Nature communications
Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and need to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment ...

Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites.

Nature communications
Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, yet our limited knowledge about the regulation and function of most phosphosites hampers the extraction of meaningful biological insights. To address th...

Robust enzyme discovery and engineering with deep learning using CataPro.

Nature communications
Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased ...

TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.

Nature communications
Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Nature communications
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and v...

Miniature origami robot for various biological micromanipulations.

Nature communications
Robotic micromanipulation is widely applied in biological research and medical procedures, providing a level of operational precision and stability beyond human capability. Compared with traditional micromanipulators that require assembly from many p...

Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.

Nature communications
Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RN...

Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.

Nature communications
Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformities and is increasingly sought for cosmetic purposes. Accurate prediction of surgical outcomes is essential for selecting the optimal treatment plan a...