Nature Machine Intelligence

Deep learning enhances the prediction of HLA class I-presented CD8(+) T cell epitopes in foreign pathogens.

深度学习增强了对外来病原体中 HLA I 类呈递的 CD8(+) T 细胞表位的预测

Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

通过 pySTED 进行真实模拟，开发 AI 辅助显微镜框架

Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling

利用基于生成式 AI 的虚拟多路复用肿瘤分析加速组织病理学工作流程

A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features

利用纳米级核特征识别细胞异质性的深度学习方法

Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity

深度神经网络预测 I 类主要组织相容性复合体表位呈递和转移，学习新表位免疫原性

Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models

使用患者来源的干细胞模型预测帕金森病的机制亚型

Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics

跨通道荧光学习改善阿尔茨海默病治疗的高内涵筛选

Deep learning models for predicting RNA degradation via dual crowdsourcing

通过双重众包预测 RNA 降解的深度学习模型

Prediction of drug combination effects with a minimal set of experiments

通过最少的实验预测药物组合效应