Unlocking human immune system complexity through AI

doi:10.1038/s41592-024-02351-1

2024-08-09

PRE

DOI：10.1038/s41592-024-02351-1

原文链接

原文求助

摘要

En 中文

Advancements in artificial intelligence (AI) have led to unprecedented success in modeling technically challenging domains including language, audio, image and video understanding. Here we discuss the opportunities represented by recent AI methods to advance immunology research.

期刊

Nature Methods

IF:

32.1

论文数:

7.3K

被引数:

12.7W

机构

Stanford University

学者数:

9.6W

论文数: 8.2W

被引数: 17.0W

引用论文

The inflammatory status of the elderly: The intestinal contribution

Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis

IF0

2010-08-01

PREAI

Eduardo J. Schiffrin; John E. Morley; Anne Donnet-Hughes; Yves Guigoz

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions 将机械免疫学知识集成到机器学习管道中可以改善预测

NATURE MACHINE INTELLIGENCE

IF23.9

2020-10-12

OAAI

Culos, Anthony; Tsai, Amy S.; Stanley, Natalie; Becker, Martin; Ghaemi, Mohammad S.; McIlwain, David R.; Fallahzadeh, Ramin; Tanada, Athena; Nassar, Huda; Espinosa, Camilo; Xenochristou, Maria; Ganio, Edward; Peterson, Laura; Han, Xiaoyuan; Stelzer, Ina A.; Ando, Kazuo; Gaudilliere, Dyani; Phongpreecha, Thanaphong; Maric, Ivana; Chang, Alan L.; Shaw, Gary M.; Stevenson, David K.; Bendall, Sean; Davis, Kara L.; Fantl, Wendy; Nolan, Garry P.; Hastie, Trevor; Tibshirani, Robert; Angst, Martin S.; Gaudilliere, Brice; Aghaeepour, Nima

Active learning for optimal intervention design in causal models

NATURE MACHINE INTELLIGENCE

IF23.9

2023-10-02