AI Medical Compendium Journal:
Nature communications

Showing 91 to 100 of 854 articles

Multi-axis robotic forceps with decoupled pneumatic actuation and force sensing for cochlear implantation.

Nature communications
Delicate manual microsurgeries rely on sufficient hands-on experience for safe manipulations. Automated surgical devices can enhance the effectiveness, but developing high-resolution, multi-axis force-sensing devices for micro operations remains chal...

Unraveling microglial spatial organization in the developing human brain with DeepCellMap, a deep learning approach coupled with spatial statistics.

Nature communications
Mapping cellular organization in the developing brain presents significant challenges due to the multidimensional nature of the data, characterized by complex spatial patterns that are difficult to interpret without high-throughput tools. Here, we pr...

Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging.

Nature communications
Normative representation learning focuses on understanding the typical anatomical distributions from large datasets of medical scans from healthy individuals. Generative Artificial Intelligence (AI) leverages this attribute to synthesize images that ...

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Nature communications
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...

Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses.

Nature communications
Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and distinguishing aggressive cancers from indolent ones is key to better treatment selection. We analyze 132...

Human mobility is well described by closed-form gravity-like models learned automatically from data.

Nature communications
Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. However, our understanding and ability to model flows between urban areas are still incomplete. At one end of the mod...

A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.

Nature communications
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory ...

Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.

Nature communications
Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well as aiding the development of therapeutic strategies. Effector predictors utilizing pre-trained protei...

Deep learning to decode sites of RNA translation in normal and cancerous tissues.

Nature communications
The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Accurate delineation of RNA translation variation represents a significant challenge due to the complexity of the process a...

Hybrid neural networks for continual learning inspired by corticohippocampal circuits.

Nature communications
Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal...