AI Medical Compendium Journal:
Nature communications

Showing 111 to 120 of 854 articles

Incremental accumulation of linguistic context in artificial and biological neural networks.

Nature communications
Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we s...

Multidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points.

Nature communications
Biological neural systems seamlessly integrate perception and action, a feat not efficiently replicated in current physically separated designs of neural-imitating electronics. This segregation hinders coordination and functionality within the neurom...

Mitochondrial segmentation and function prediction in live-cell images with deep learning.

Nature communications
Mitochondrial morphology and function are intrinsically linked, indicating the opportunity to predict functions by analyzing morphological features in live-cell imaging. Herein, we introduce MoDL, a deep learning algorithm for mitochondrial image seg...

Understanding the spread of agriculture in the Western Mediterranean (6th-3rd millennia BC) with Machine Learning tools.

Nature communications
The first Neolithic farmers arrived in the Western Mediterranean area from the East. They established settlements in coastal areas and over time migrated to new environments, adapting to changing ecological and climatic conditions. While farming prac...

Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings.

Nature communications
Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells' electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer ...

Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy.

Nature communications
The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (i...

Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.

Nature communications
Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mech...

Coverage bias in small molecule machine learning.

Nature communications
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end mo...

Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception.

Nature communications
Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple 'ru...

Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data.

Nature communications
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentati...