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Deep Learning

All articles tagged with #deep learning

27-year video reshapes understanding of a distant blazar jet
astronomy18 days ago

27-year video reshapes understanding of a distant blazar jet

Scientists created a 27-year video reconstruction of the jet from blazar 3C 345, using very long baseline interferometry (VLBI) and a deep‑learning model called Kine to map brightness over time. The continuous video reveals that the bright jet features move at speeds nearly equal to the bulk plasma flow, challenging the idea that knots are faster shocks, and suggesting revisions to jet dynamics models. The method, which delivers two orders of magnitude better dynamic range and four times better resolution, will be applied to other blazars, with the study published in Nature (August 2026).

via Space|
#3c-345#astronomy#black-hole-jet
AI-detected ECG signature reveals new high-risk group for sudden cardiac death
science3 months ago

AI-detected ECG signature reveals new high-risk group for sudden cardiac death

A Swedish deep-learning model trained on hundreds of thousands of ECGs linked to death data identifies a 2.2% high-risk group with a 7.0% annual Sudden Cardiac Death rate, most of whom would not be flagged by reduced left ventricular ejection fraction (LVEF); external validation in the US and Taiwan shows the model generalizes to predict arrhythmic events; a generative model visualizes a concrete ECG biomarker and implicates conduction changes related to fibrosis; these findings point to a sizable, previously unrecognized population that could potentially benefit from defibrillators and warrant randomized trials.

via Nature|
#biomarker#deep-learning#defibrillator
AI uncovers antimicrobial fragments hidden in prions
science3 months ago

AI uncovers antimicrobial fragments hidden in prions

Researchers used APEX 1.1 to mine 19.3 million fragments from 2,897 prion-related proteins, identifying 1,179 prionins predicted to be antimicrobial (median MIC ≤ 64 μM). Out of 75 synthesized, 59 inhibited at least one pathogen and 42 achieved MIC ≤16 μM, mainly against Gram-negative bacteria, often via membrane disruption as shown by NPN and DiSC3-5 assays. In vivo, two lead prionins reduced bacterial burden in a mouse Acinetobacter baumannii skin infection model with favorable selectivity. The study suggests prion-related proteins are a rich source of cryptic AMP leads, though physiological roles remain unproven.

via Nature|
#antibiotic-discovery#antimicrobial-peptides#deep-learning
AI Uncovers Hidden Deep Quakes Beneath East Antarctica
science3 months ago

AI Uncovers Hidden Deep Quakes Beneath East Antarctica

Researchers analyzed data from 49 seismic stations across East Antarctica with a deep-learning detector, identifying about 510 deep, intermediate-depth earthquakes (magnitudes 1.6–3.5) clustered 100–150 km beneath the David Glacier. Occurring far from plate boundaries, these intraplate quakes are likely driven by rock bending from high-temperature mantle and a nearby lithospheric boundary between East and West Antarctica, illustrating how AI can reveal hidden seismic activity.

via ScienceAlert|
#antarctica#deep-learning#earthquakes
AI-Detected Quakes Reveal Deep Activity Beneath Antarctica
science3 months ago

AI-Detected Quakes Reveal Deep Activity Beneath Antarctica

Using deep-learning analysis of seismology data, researchers identified hundreds of small intraplate earthquakes under Antarctica’s David Glacier at depths around 70 km, with magnitudes between 1.6 and 3.5. The events, occurring away from plate boundaries, suggest complex lithospheric dynamics and could prompt revisions to plate tectonics theory, with AI tools potentially revealing similar activity globally.

via Futurism|
#antarctica#deep-learning#earthquakes
Thymic Health Emerges as a Pan-Cancer Predictor of Immunotherapy Response
science6 months ago

Thymic Health Emerges as a Pan-Cancer Predictor of Immunotherapy Response

A large real-world and TRACERx NSCLC study shows radiographically assessed thymic health, quantified by a deep-learning CT system, strongly associates with better progression-free and overall survival in patients treated with immune checkpoint inhibitors across several cancers. Thymic health performed comparably to established biomarkers like PD-L1 and tumor mutational burden in prognostication, and its link to T cell receptor diversity and immune pathways supports its relevance as a host-immune biomarker and potential target for timing and strategies to boost immunotherapy efficacy.

via Nature|
#cancer-biomarkers#ct-imaging#deep-learning
MPRA-powered CNN reveals the cell-type grammar of human promoters and enables synthetic promoter design
science8 months ago

MPRA-powered CNN reveals the cell-type grammar of human promoters and enables synthetic promoter design

PARM, a cell-type-specific convolutional neural network trained on massively parallel reporter assays, predicts autonomous promoter activity from DNA sequences across multiple cell types and stimuli. It identifies functional TF motifs, uncovers a position-dependent regulatory grammar around the transcription start site, distinguishes activating and repressing factors, and can design synthetic promoters. The approach validates predictions with ISM and motif-insertion experiments, offering mechanistic insights into promoter regulation and potential applications in disease research and personalized medicine.

via Nature|
#deep-learning#gene-regulation#mpra
DeepMet: AI models illuminate unseen mammalian metabolites
science8 months ago

DeepMet: AI models illuminate unseen mammalian metabolites

A new chemical language-model approach, DeepMet, learns from known human metabolites to generate metabolite-like structures and prioritize plausible, yet-unrecognized mammalian metabolites. By coupling DeepMet with mass-spec data and MS/MS prediction (CFM-ID), the method enables de novo generation and targeted discovery of metabolites, identifying 16 previously unrecognized mouse tissue metabolites and 17 metabolites in human biofluids, and correctly predicting 252 of 313 HMDB 5.0 additions (81%). The team further improves annotation with a meta-learning framework that integrates retention times and isotope patterns, achieving about 70% accuracy in a mouse dataset. They also release a web app and Snakemake pipeline to extend the approach, highlighting DeepMet’s potential to fill gaps in mammalian metabolome maps while noting limitations such as its focus on metabolite-like chemical space and isomer ambiguity.

via Nature|
#artificial-intelligence#deep-learning#mammalian-metabolism
AI Model Uses Sleep Data to Predict Long-Term Disease Risks
health-and-medicine9 months ago

AI Model Uses Sleep Data to Predict Long-Term Disease Risks

The article introduces SleepFM, a large-scale foundation model trained on over 585,000 hours of sleep data from 65,000+ participants, which captures complex sleep physiology across multiple modalities and demonstrates strong predictive power for a wide range of diseases, outperforming traditional models and showing robust generalization across datasets and time.

via Nature|
#deep-learning#disease-prediction#foundation-model
Revival of 'World Models' in AI Innovation
technology1 year ago

Revival of 'World Models' in AI Innovation

The article discusses the resurgence of 'world models' in AI research, a concept dating back to the 1940s, which involves creating internal representations of the environment to improve AI decision-making and robustness. While early attempts relied on handcrafted models, modern deep learning approaches aim to develop these models automatically, though current systems often rely on heuristics rather than coherent representations. Developing effective world models is seen as crucial for advancing AI safety, reliability, and interpretability, with various approaches being explored to achieve this goal.

via Quanta Magazine|
#agi#ai#deep-learning