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Link to original content: https://www.nature.com/natmachintell.rss
Nature Machine Intelligence Nature Machine Intelligence will publish high-quality original research and reviews in a wide range of topics in machine learning, robotics and AI. The journal will also explore and discuss the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where machine intelligence can augment human capabilities and knowledge in fields such as scientific discovery, healthcare, medical diagnostics and safe and sustainable cities, transport and agriculture. At the same time, many important questions on ethical, social and legal issues arise, especially given the fast pace of developments Nature Machine Intelligence will provide a platform to discuss these wide implications — encouraging a cross-disciplinary dialogue — with Comments, News Features, News &amp; Views articles and also Correspondence. http://feeds.nature.com/natmachintell/rss/current Nature Publishing Group en © 2024 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Machine Intelligence © 2024 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. permissions@nature.com Nature Machine Intelligence https://www.nature.com/uploads/product/natmachintell/rss.gif http://feeds.nature.com/natmachintell/rss/current <![CDATA[General-purpose foundation models for increased autonomy in robot-assisted surgery]]> https://www.nature.com/articles/s42256-024-00917-4 Nature Machine Intelligence, Published online: 01 November 2024; doi:10.1038/s42256-024-00917-4

Schmidgall et al. describe a pathway for building general-purpose machine learning models for robot-assisted surgery, including mechanisms for avoiding risk and handing over control to surgeons, and improving safety and outcomes beyond demonstration data.]]> Samuel SchmidgallJi Woong KimAlan KuntzAhmed Ezzat GhaziAxel Krieger doi:10.1038/s42256-024-00917-4 Nature Machine Intelligence, Published online: 2024-11-01; | doi:10.1038/s42256-024-00917-4 2024-11-01 Nature Machine Intelligence 10.1038/s42256-024-00917-4 https://www.nature.com/articles/s42256-024-00917-4 <![CDATA[Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging]]> https://www.nature.com/articles/s42256-024-00912-9 Nature Machine Intelligence, Published online: 30 October 2024; doi:10.1038/s42256-024-00912-9

Automating the image analysis process for oncologic whole-body positron emission tomography–computed tomography data is a key area of interest. Gatidis et al. describe the autoPET 2022 challenge, an international competition focused on the segmentation of metabolically active tumour lesions, aiming to advance techniques in the field.]]> Sergios GatidisMarcel FrühMatthias P. FabritiusSijing GuKonstantin NikolaouChristian La FougèreJin YeJunjun HeYige PengLei BiJun MaBo WangJia ZhangYukun HuangLars HeiligerZdravko MarinovRainer StiefelhagenJan EggerJens KleesiekLudovic SibilleLei XiangSimone BendazzoliMehdi AstarakiMichael IngrischClemens C. CyranThomas Küstner doi:10.1038/s42256-024-00912-9 Nature Machine Intelligence, Published online: 2024-10-30; | doi:10.1038/s42256-024-00912-9 2024-10-30 Nature Machine Intelligence 10.1038/s42256-024-00912-9 https://www.nature.com/articles/s42256-024-00912-9 <![CDATA[Author Correction: Predicting equilibrium distributions for molecular systems with deep learning]]> https://www.nature.com/articles/s42256-024-00933-4 Nature Machine Intelligence, Published online: 29 October 2024; doi:10.1038/s42256-024-00933-4

Author Correction: Predicting equilibrium distributions for molecular systems with deep learning]]> Shuxin ZhengJiyan HeChang LiuYu ShiZiheng LuWeitao FengFusong JuJiaxi WangJianwei ZhuYaosen MinHe ZhangShidi TangHongxia HaoPeiran JinChi ChenFrank NoéHaiguang LiuTie-Yan Liu doi:10.1038/s42256-024-00933-4 Nature Machine Intelligence, Published online: 2024-10-29; | doi:10.1038/s42256-024-00933-4 2024-10-29 Nature Machine Intelligence 10.1038/s42256-024-00933-4 https://www.nature.com/articles/s42256-024-00933-4 <![CDATA[Deep learning prediction of ribosome profiling with Translatomer reveals translational regulation and interprets disease variants]]> https://www.nature.com/articles/s42256-024-00915-6 Nature Machine Intelligence, Published online: 23 October 2024; doi:10.1038/s42256-024-00915-6

A transformer-based approach called Translatomer is presented, which models cell-type-specific translation from messenger RNA expression and gene sequence, bridging the gap between messenger RNA and protein levels as well as providing a mechanistic insight into the genetic regulation of translation.]]> Jialin HeLei XiongShaohui ShiChengyu LiKexuan ChenQianchen FangJiuhong NanKe DingYuanhui MaoCarles A. BoixXinyang HuManolis KellisJingyun LiXushen Xiong doi:10.1038/s42256-024-00915-6 Nature Machine Intelligence, Published online: 2024-10-23; | doi:10.1038/s42256-024-00915-6 2024-10-23 Nature Machine Intelligence 10.1038/s42256-024-00915-6 https://www.nature.com/articles/s42256-024-00915-6 <![CDATA[Epitope-anchored contrastive transfer learning for paired CD8<sup>+</sup> T cell receptor–antigen recognition]]> https://www.nature.com/articles/s42256-024-00913-8 Nature Machine Intelligence, Published online: 22 October 2024; doi:10.1038/s42256-024-00913-8

Accurate prediction of T cell receptor (TCR)–antigen recognition remains a challenge. Zhang et al. propose a contrastive transfer learning model to predict TCR–pMHC binding that enables interpretable analyses of epitope-specific T cells and can decipher residue-level interactions.]]> + T cell receptor–antigen recognition]]> Yumeng ZhangZhikang WangYunzhe JiangDene R. LittlerMark GersteinAnthony W. PurcellJamie RossjohnHong-Yu OuJiangning Song doi:10.1038/s42256-024-00913-8 Nature Machine Intelligence, Published online: 2024-10-22; | doi:10.1038/s42256-024-00913-8 2024-10-22 Nature Machine Intelligence 10.1038/s42256-024-00913-8 https://www.nature.com/articles/s42256-024-00913-8 <![CDATA[Pick your AI poison]]> https://www.nature.com/articles/s42256-024-00921-8 Nature Machine Intelligence, Published online: 21 October 2024; doi:10.1038/s42256-024-00921-8

Distinguishing between real and fabricated facts has long been a societal challenge. As the Internet becomes increasingly littered with AI-generated content, the need for curation and safeguarding of high-quality data and information is more crucial than ever.]]> doi:10.1038/s42256-024-00921-8 Nature Machine Intelligence, Published online: 2024-10-21; | doi:10.1038/s42256-024-00921-8 2024-10-21 Nature Machine Intelligence 10.1038/s42256-024-00921-8 https://www.nature.com/articles/s42256-024-00921-8 <![CDATA[Leveraging language model for advanced multiproperty molecular optimization via prompt engineering]]> https://www.nature.com/articles/s42256-024-00916-5 Nature Machine Intelligence, Published online: 21 October 2024; doi:10.1038/s42256-024-00916-5

Designing molecules in drug design is challenging as it requires optimizing multiple, potentially competing qualities. Wu and colleagues present a prompt-based molecule optimization method that can be trained from single-property data.]]> Zhenxing WuOdin ZhangXiaorui WangLi FuHuifeng ZhaoJike WangHongyan DuDejun JiangYafeng DengDongsheng CaoChang-Yu HsiehTingjun Hou doi:10.1038/s42256-024-00916-5 Nature Machine Intelligence, Published online: 2024-10-21; | doi:10.1038/s42256-024-00916-5 2024-10-21 Nature Machine Intelligence 10.1038/s42256-024-00916-5 https://www.nature.com/articles/s42256-024-00916-5 <![CDATA[Blending neural operators and relaxation methods in PDE numerical solvers]]> https://www.nature.com/articles/s42256-024-00910-x Nature Machine Intelligence, Published online: 17 October 2024; doi:10.1038/s42256-024-00910-x

Neural-network-based solvers for partial differential equations (PDEs) suffer from difficulties tackling high-frequency modes when learning complex functions, whereas for classical solvers it is more difficult to handle low-frequency modes. Zhang and colleagues propose a hybrid numerical PDE solver by combining a Deep Operator Network with traditional relaxation methods, leading to balanced convergence across the eigenmode spectrum for a wide range of PDEs.]]> Enrui ZhangAdar KahanaAlena KopaničákováEli TurkelRishikesh RanadeJay PathakGeorge Em Karniadakis doi:10.1038/s42256-024-00910-x Nature Machine Intelligence, Published online: 2024-10-17; | doi:10.1038/s42256-024-00910-x 2024-10-17 Nature Machine Intelligence 10.1038/s42256-024-00910-x https://www.nature.com/articles/s42256-024-00910-x