AI Medical Compendium Journal:
Proceedings of the National Academy of Sciences of the United States of America

Showing 71 to 80 of 333 articles

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Proceedings of the National Academy of Sciences of the United States of America
Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal rema...

Exposure to automation explains religious declines.

Proceedings of the National Academy of Sciences of the United States of America
The global decline of religiosity represents one of the most significant societal shifts in recent history. After millennia of near-universal religious identification, the world is experiencing a regionally uneven trend toward secularization. We prop...

Thinking about God increases acceptance of artificial intelligence in decision-making.

Proceedings of the National Academy of Sciences of the United States of America
Thinking about God promotes greater acceptance of Artificial intelligence (AI)-based recommendations. Eight preregistered experiments ( = 2,462) reveal that when God is salient, people are more willing to consider AI-based recommendations than when G...

Multitasking via baseline control in recurrent neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas, and can be modeled as long-range projections regulating the mean and variance of baseline input currents. What are the computational benefit...

The transformative power of transformers in protein structure prediction.

Proceedings of the National Academy of Sciences of the United States of America
Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction...

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing.

Proceedings of the National Academy of Sciences of the United States of America
Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical res...

Intrinsic neural diversity quenches the dynamic volatility of neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enr...

More than just pattern recognition: Prediction of uncommon protein structure features by AI methods.

Proceedings of the National Academy of Sciences of the United States of America
The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the...

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Proceedings of the National Academy of Sciences of the United States of America
Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. D...

Development potential of nanoenabled agriculture projected using machine learning.

Proceedings of the National Academy of Sciences of the United States of America
The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 data...