AIMC Topic: Diffusion

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CGBack: Diffusion Model for Backmapping Large-Scale and Complex Coarse-Grained Molecular Systems.

Journal of chemical information and modeling
Molecular dynamics simulations based on coarse-grained (CG) models are used to accelerate conformational dynamics of biomolecules and other chemical systems with reduced computational costs. CG models achieve this by discarding atomic information nec...

Multi-scale diffusion model for underwater image restoration and enhancement.

PloS one
BACKGROUND: Underwater environments face challenges with image degradation due to light absorption and scattering, resulting in blurring, reduced contrast, and color distortion. This significantly impacts underwater exploration and environmental moni...

Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus.

Journal of nanobiotechnology
Biosimilar artificial mucus models that mimic native mucus facilitate efficient, lab-based drug diffusion studies, addressing the costly and challenging preclinical phase of drug development, especially for nano- and micro-scale particle-based coloni...

DiffRaman: A conditional latent denoising diffusion probabilistic model for enhancing bacterial identification via Raman spectra generation under limited data.

Analytica chimica acta
Raman spectroscopy has attracted significant attention in various biochemical detection fields, especially in the rapid identification of pathogenic bacteria. The integration of this technology with deep learning to facilitate automated bacterial Ram...

Diff-SE: A Diffusion-Augmented Contrastive Learning Framework for Super-Enhancer Prediction.

Journal of chemical information and modeling
Super-enhancers (SEs) are cis-regulatory elements that play crucial roles in gene expression and are implicated in diseases such as cancer and Alzheimer's. Traditional identification methods rely on ChIP-seq experiments, which are costly and time-con...

MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.

Physics in medicine and biology
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...

Pixel super-resolved virtual staining of label-free tissue using diffusion models.

Nature communications
Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based pixel super-resolution virtual staini...

DihedralsDiff: A Diffusion Conformation Generation Model That Unifies Local and Global Molecular Structures.

Journal of chemical information and modeling
Significant advancements have been made in utilizing artificial intelligence to learn to generate molecular conformations, which has greatly facilitated the discovery of drug molecules. In particular, the rapid development of diffusion models has led...

Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function.

Nature methods
Subcellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with unprecedented precision. However, the agnostic and a...

Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images.

Medical physics
BACKGROUND: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemorrhages, using only healthy training data. Recent advancements in generative models have achieved suc...