Quintina Campbell,Sam Cox,Jorge Medina et al.
Quintina Campbell et al.
Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLMs) have demonstrated success in automating complex scientific tasks us...
CAP: Commutative algebra prediction of protein-nucleic acid binding affinities [0.03%]
基于交换代数的蛋白质-核酸结合亲和力预测模型
Mushal Zia,Faisal Suwayyid,Yuta Hozumi et al.
Mushal Zia et al.
An accurate prediction of protein-nucleic acid binding affinity is vital for deciphering genomic processes, yet existing approaches often struggle in reconciling high accuracy with interpretability and computational efficiency. In this stud...
FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model [0.03%]
基于频率解耦扩散模型的无监督医学图像转换（FDDM）
Yunxiang Li,Hua-Chieh Shao,Xiaoxue Qian et al.
Yunxiang Li et al.
Diffusion models have demonstrated significant potential in producing high-quality images in medical image translation to aid disease diagnosis, localization, and treatment. Nevertheless, current diffusion models often fall short when it co...
Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2 [0.03%]
基于深度膨胀卷积适配器的医学对象跟踪和分割方法（使用Segment Anything Model 2）
Guoping Xu,Christopher Kabat,You Zhang
Guoping Xu
Recent advances in medical image segmentation have been driven by deep learning; however, most existing methods remain limited by modality-specific designs and exhibit poor adaptability to dynamic medical imaging scenarios. The Segment Anyt...
Generative diffusion model surrogates for mechanistic agent-based biological models [0.03%]
生成扩散模型代理用于机制主体基于生物学模型
Tien Comlekoglu,J Quetzalcoatl Toledo-Marín,Douglas W DeSimone et al.
Tien Comlekoglu et al.
Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and inter...
Medical Image Segmentation Assisted with Clinical Inputs via Language Encoder in A Deep Learning Framework [0.03%]
基于深度学习框架的医学图像分割方法研究及应用
Hengrui Zhao,Biling Wang,Deepkumar Mistry et al.
Hengrui Zhao et al.
Introduction: Auto-segmentation of tumor volumes and organs at risk (OARs) is a critical step in cancer radiotherapy treatment planning, where rapid, precise adjustments to treatment plans are required to match the patien...
Deep Unsupervised Clustering for Prostate Auto-segmentation With and Without Hydrogel Spacer [0.03%]
基于深度无监督聚类的前列腺自动分割方法（含与不含水凝胶间隔物）
Hengrui Zhao,Biling Wang,Michael Dohopolski et al.
Hengrui Zhao et al.
Introduction: Clinical datasets for training deep learning (DL) models often exhibit high levels of heterogeneity due to differences such as patient characteristics, new medical techniques, and physician preferences. In r...
32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery [0.03%]
材料科学和化学中LLM应用的32个示例：迈向自动化、助手、代理及加速科学研究
Yoel Zimmermann,Adib Bazgir,Alexander Al-Feghali et al.
Yoel Zimmermann et al.
Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent develop...
Graph prolongation convolutional networks: explicitly multiscale machine learning on graphs with applications to modeling of cytoskeleton [0.03%]
图延长卷积网络：图上显式多尺度机器学习及其在骨架建模中的应用
Cory B Scott,Eric Mjolsness
Cory B Scott
We define a novel type of ensemble graph convolutional network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its fina...
Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures [0.03%]
超越欧几里得：用几何、拓扑和代数结构插图介绍现代机器学习
Mathilde Papillon,Sophia Sanborn,Johan Mathe et al.
Mathilde Papillon et al.
The enduring legacy of Euclidean geometry underpins classical machine learning, which, for decades, has been primarily developed for data lying in Euclidean space. Yet, modern machine learning increasingly encounters richly structured data ...