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今天给大家介绍来自德国蒂宾根大学的Robert Geirhos和加拿大多伦多大学的Claudio Michaelis等人发表在Nature Machine Intelligence的文章“Shortcut learning in deep neural networks”。文章认为有很多种深度学习（...

Nat. Mach. Intell. | 人工智能的透明度和可重复性

今天给大家介绍多伦多大学的研究人员发表在nature machine intelligence上的一篇文章。文章指出McKinney等人利用AI在乳腺癌筛选上的工作，缺乏研究方法和代码实现的细节，阻碍了透明且可重复（transparent and reproducible）的AI研究，文...

Nat. Mach. Intell. | 基于神经网络的迁移学习用于单细胞RNA-seq分析中的聚类和细胞类型分类...

今天给大家介绍由美国宾夕法尼亚大学佩雷尔曼医学院生物统计学，流行病学和信息学系Jian Hu等人在《Nature Machine Intelligence》上发表了一篇名为“Iterative transfer learning with neural network for clustering ...

Nat. Mach. Intell. | 可解释性人工智能(xAI)遇上药物发现

今天给大家介绍瑞士苏黎世联邦理工学院化学与应用生物科学系 Gisbert Schneider等人在Nature Machine Intelligence上发表的文章“Drug discovery with explainable artificial intelligence”。本文综述总结了可解释...

Nat. Mach. Intell. | 华科同济医学院剑桥联手推出新冠预测模型！

今天给大家介绍华中科技大学同济医学院及剑桥大学联合发表在Nature Machine Intelligence的一篇文章。文章中作者提出了一个基于XGBoost机器学习的模型，可以提前10天以上预测患者的死亡率，准确率超过90%，从而实现对COVID-19患者的检测、早期干预，并有可能降低死亡率。1...

Nat. Mach. Intell. | FFPred-GAN：“以假乱真“—基于GAN创建合成特征样本改进蛋白质功能预测...

今天给大家介绍伦敦大学学院David T. Jones 教授课题组发表在Nature Machine Intelligence 的一篇文章。文章中指出，现存的蛋白质功能预测方法受限于训练样本量的瓶颈，为了解决这个问题，作者提出了一种新的基于生成对抗网络的方法FFPred-GAN。FFPred-GAN...

Nat. Mach. Intell. | 生物医学关系抽取的机器学习新框架

今天给大家介绍的是清华大学曾坚阳教授课题组在Nature Machine Intelligence杂志上发表的一篇关于生物医学关系抽取的文章。在文中，作者提出了一种从大规模文献库中自动提取生物医学关系的机器学习框架—BERE。BERE使用混合编码网络从语义和句法两个方面更好地表示每个句子，并在考虑所...

Nat. Mach. Intell. | 基于深度强化学习寻找网络中的关键节点

今天给大家介绍哈佛大学Yang-Yu Liu课题组和加利福尼亚大学洛杉矶分校Yizhou Sun课题组发表在nature machine intelligence上的一篇文章“Finding key players in complex networks through deep reinforce...

Nat. Mach. Intell. | 利用条件循环神经网络生成特定性质分子

今天给大家介绍瑞士知名药企阿斯利康和伯尔尼大学的 Esben Jannik Bjerrum团队在Nature Machine Intelligence上的一篇论文。该研究提出基于分子SMILES表示的条件循环神经网络，输入目标性质，模型可直接生成具有对应性质的分子。1背景机器学习对生物和化学领域有着...

Nat. Mach. Intell. | 快速的蛋白质结构从头预测

今天给大家介绍一篇Nature Machine Intelligence期刊的论文“AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction”，该工作由清华大学龚海鹏课题组完成。本文提出...

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