T Li,L Biferale,F Bonaccorso et al.
T Li et al.
Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere, oceans and astrophysics. Despite exceptional theoretical, numeric...
A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics [0.03%]
用于揭示正常心脏动态的个性化时间分辨三维网格生成模型
Mengyun Qiao,Kathryn A McGurk,Shuo Wang et al.
Mengyun Qiao et al.
Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic, ...
Embodied large language models enable robots to complete complex tasks in unpredictable environments [0.03%]
具身大语言模型使机器人能够在不可预测的环境中完成复杂任务
Ruaridh Mon-Williams,Gen Li,Ran Long et al.
Ruaridh Mon-Williams et al.
Completing complex tasks in unpredictable settings challenges robotic systems, requiring a step change in machine intelligence. Sensorimotor abilities are considered integral to human intelligence. Thus, biologically inspired machine intell...
Data-Driven Discovery of Movement-Linked Heterogeneity in Neurodegenerative Diseases [0.03%]
数据驱动的神经退行性疾病中与运动相关的异质性发现
Mark Endo,Favour Nerrise,Qingyu Zhao et al.
Mark Endo et al.
Neurodegenerative diseases manifest different motor and cognitive signs and symptoms that are highly heterogeneous. Parsing these heterogeneities may lead to an improved understanding of underlying disease mechanisms; however current method...
Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modelling [0.03%]
基于神经药代/药动学模型的深度学习预测早期数据的患者反应时间过程
James Lu,Brendan Bender,Jin Y Jin et al.
James Lu et al.
Longitudinal analyses of patient response time courses following doses of therapeutics are currently performed using pharmacokinetic/pharmacodynamic (PK/PD) methodologies, which require considerable human experience and expertise in the mod...
Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens [0.03%]
深度学习提升对外源病原体中HLA-I类呈递的CD8+T细胞表位的预测能力
Jeremy Wohlwend,Anusha Nathan,Nitan Shalon et al.
Jeremy Wohlwend et al.
Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to com...
A machine learning approach to leveraging electronic health records for enhanced omics analysis [0.03%]
利用电子健康记录增强基因组分析的机器学习方法
Samson J Mataraso,Camilo A Espinosa,David Seong et al.
Samson J Mataraso et al.
Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited...
Discovering fully semantic representations via centroid- and orientation-aware feature learning [0.03%]
通过质心和方向感知特征学习发现完全语义表示
Jaehoon Cha,Jinhae Park,Samuel Pinilla et al.
Jaehoon Cha et al.
Learning meaningful representations of images in scientific domains that are robust to variations in centroids and orientations remains an important challenge. Here we introduce centroid- and orientation-aware disentangling autoencoder (COD...
Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models [0.03%]
从基因组深度神经网络使用代理模型解释顺式调控机制
Evan E Seitz,David M McCandlish,Justin B Kinney et al.
Evan E Seitz et al.
Deep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. However, elucidating underlying biological mechanisms from genomic DNNs remains challenging. Existing interpretability methods, such as ...
The design space of E(3)-equivariant atom-centred interatomic potentials [0.03%]
E(3)等变原子中心间原子势能的设计空间
Ilyes Batatia,Simon Batzner,Dávid Péter Kovács et al.
Ilyes Batatia et al.
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has p...