Tag

Deep Learning

All articles tagged with #deep learning

science23 days ago•87 min saved

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

science1 month ago•121 min saved

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

health-and-medicine1 month ago•54 min saved

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

science2 months ago•5 min saved

MIT AI Model Accurately Predicts Cell Behavior in Fruit Fly Embryos

MIT engineers developed a deep-learning model that predicts, with 90% accuracy, how individual cells in a fruit fly embryo fold, divide, and rearrange during early development, potentially aiding in understanding tissue formation and early disease detection.

via MIT News|

#biomedical-research #cell-development #deep-learning

science4 months ago•2 min saved

AI-Driven Framework Accelerates Discovery of Cell State-Correcting Medicines

An AI model called DrugReflector, trained on gene expression data, significantly accelerates drug discovery by more effectively identifying promising compounds, doubling success rates when iteratively refined, and offering a powerful new tool for developing therapies.

via Nature|

#ai #biotechnology #deep-learning

technology4 months ago•8 min saved

Trimodal Protein Model Enhances Search Capabilities

A new trimodal protein language model enhances the accuracy and efficiency of protein searches, leveraging advanced deep learning techniques to improve understanding of protein structures and functions.

via Nature|

#bioinformatics #deep-learning #protein-language-model

technology5 months ago•5 min saved

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

science6 months ago•75 min saved

BindCraft Enables One-Shot Design of Functional Protein Binders

BindCraft is a novel computational pipeline that leverages deep learning, specifically AlphaFold2, to de novo design functional protein binders targeting diverse proteins, including challenging membrane receptors, allergens, and nucleases, with high success rates and potential therapeutic applications.

via Nature|

#bindcraft #deep-learning #protein-design

science6 months ago•5 min saved

Simpler Models Outperform Deep Learning in Climate Prediction

A study by MIT researchers shows that simpler, physics-based models can outperform complex deep-learning models in climate prediction, especially for regional temperature estimates, highlighting the importance of appropriate benchmarking and problem-specific modeling approaches in climate science.

via MIT News|

#ai-models #benchmarking-techniques #climate-emulators

science6 months ago•11 min saved

AI Advances in Decoding Genome Grammar Beyond AlphaFold

AI tools like Evo, regLM, and AlphaGenome are advancing the decoding of the genome's regulatory regions, especially the non-coding 'dark matter' DNA, by predicting functions, interactions, and impacts of genetic variants, thus deepening our understanding of gene regulation and expression.

via Nature|

#ai #deep-learning #genome

health7 months ago•5 min saved

AI Revolutionizes Heart Disease Detection and Prevention

Researchers have developed EchoNext, an AI tool that analyzes standard ECGs to detect hidden structural heart diseases more accurately than cardiologists, potentially transforming routine cardiac tests into early screening methods for serious heart conditions.

via SciTechDaily|

#ai #deep-learning #ecg

technology9 months ago•11 min saved

A Beginner's Guide to AI Terms and Concepts

This article provides a comprehensive glossary of key AI terms, explaining concepts like AGI, AI agents, chain of thought, deep learning, diffusion, distillation, fine-tuning, GANs, hallucinations, inference, large language models, neural networks, training, transfer learning, and weights, to help readers understand the complex language used in AI research and development.

via TechCrunch|

#ai-glossary #artificial-intelligence #deep-learning

technology1 year ago•5 min saved

MIT's Photonic Chip Revolutionizes Energy-Efficient AI Computing

MIT researchers have developed a new photonic chip that performs deep neural network computations using light, achieving ultrafast processing speeds and high energy efficiency. This fully integrated photonic processor can handle both linear and nonlinear operations on-chip, overcoming previous limitations that required off-chip electronics. The chip's performance is comparable to traditional hardware, completing tasks in less than half a nanosecond with over 92% accuracy. This advancement could lead to faster, more efficient AI applications in fields like telecommunications and scientific research.

via MIT News|

#ai #deep-learning #energy-efficiency

technology1 year ago•3 min saved

AI Revolutionizes Rapid Disease and Cancer Detection

Researchers at Washington State University have developed an AI system that uses deep learning to identify diseases from tissue images more accurately and quickly than humans. This model, detailed in Scientific Reports, processes gigapixel histopathology slides, significantly speeding up disease research. It integrates AI, computer vision, and medicine to overcome challenges in automatic disease detection, and has shown superior performance in identifying pathologies compared to previous systems and human experts.

via Tech Explorist|

#ai #deep-learning #disease-detection

technology1 year ago•5 min saved

AI Surpasses Human Speed in Disease Detection

A deep learning AI model developed by Washington State University researchers significantly enhances the speed and accuracy of disease detection in tissue images, outperforming human pathologists in some cases. By analyzing gigapixel images with advanced neural networks, the AI reduces analysis times from months to weeks, revolutionizing research and diagnostics, particularly for cancer and gene-related illnesses. This model, already aiding animal disease research, holds transformative potential for human medical diagnostics.

via Neuroscience News|

#ai #deep-learning #disease-detection