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ORCIDNature Machine Intelligence, Volume 6
Volume 6, Number 1, 2024
- Is it five already? 1
- Andreas Kleppe
, Ole-Johan Skrede, Knut Liestøl, David J. Kerr, Håvard E. Danielsen:
Guidelines for study protocols describing predefined validations of prediction models in medical deep learning and beyond. 2-3 - Étienne Ollion, Rubing Shen, Ana Macanovic, Arnault Chatelain:
The dangers of using proprietary LLMs for research. 4-5 - Noelia Ferruz, Marinka Zitnik, Pierre-Yves Oudeyer, Emmie Hine, Nandana Sengupta, Yiyu Shi, Diana Mincu, Sebastian Porsdam Mann, Payel Das, Francesco Stella:
Anniversary AI reflections. 6-12 - Séverine Atis, Lionel Agostini:
Catching up with missing particles. 13-14 - Milena Pavlovic, Ghadi S. Al Hajj, Chakravarthi Kanduri, Johan Pensar, Mollie Wood, Ludvig Magne Sollid, Victor Greiff, Geir Kjetil Sandve:
Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. 15-24 - Yutong Sha, Yuchi Qiu, Peijie Zhou, Qing Nie:
Reconstructing growth and dynamic trajectories from single-cell transcriptomics data. 25-39 - Simone Ciceri, Lorenzo Cassani, Matteo Osella, Pietro Rotondo, Filippo Valle, Marco Gherardi:
Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization. 40-47 - Yaning Han, Ke Chen, Yunke Wang, Wenhao Liu, Zhouwei Wang, Xiaojing Wang, Chuanliang Han, Jiahui Liao, Kang Huang, Shengyuan Cai, Yiting Huang, Nan Wang, Jinxiu Li, Yangwangzi Song, Jing Li, Guo-Dong Wang, Liping Wang, Yaping Zhang, Pengfei Wei:
Multi-animal 3D social pose estimation, identification and behaviour embedding with a few-shot learning framework. 48-61 - Wei Feng, Lvwei Wang, Zaiyun Lin, Yanhao Zhu, Han Wang, Jianqiang Dong, Rong Bai, Huting Wang, Jielong Zhou, Wei Peng, Bo Huang, Wenbiao Zhou:
Generation of 3D molecules in pockets via a language model. 62-73 - Aubin Ramon, Montader Ali, Misha Atkinson, Alessio Saturnino, Kieran Didi, Cristina Visentin, Stefano Ricagno, Xing Xu, Matthew Greenig, Pietro Sormanni:
Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV. 74-91 - Sarmad Ahmad Abbasi, Awais Ahmed, Seungmin Noh, Nader Latifi Gharamaleki, Seonhyoung Kim, A. M. Masum Bulbul Chowdhury, Jin-young Kim, Salvador Pané, Bradley J. Nelson, Hongsoo Choi:
Autonomous 3D positional control of a magnetic microrobot using reinforcement learning. 92-105 - Arvin Tashakori, Zenan Jiang, Amir Servati, Saeid Soltanian, Harishkumar Narayana, Katherine Le, Caroline Nakayama, Chieh-ling Yang, Z. Jane Wang, Janice J. Eng, Peyman Servati:
Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves. 106-118 - Yutong Sha, Yuchi Qiu, Peijie Zhou, Qing Nie:
Publisher Correction: Reconstructing growth and dynamic trajectories from single-cell transcriptomics data. 119
Volume 6, Number 2, 2024
- AI protein shake-up. 121
- Katja Seeliger, Martin N. Hebart:
What comparing deep neural networks can teach us about human vision. 122-123 - Stephan Allenspach, Jan A. Hiss, Gisbert Schneider:
Neural multi-task learning in drug design. 124-137 - Suyue Lyu, Shahin Sowlati-Hashjin, Michael Garton:
Variational autoencoder for design of synthetic viral vector serotypes. 147-160 - Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit:
Leveraging large language models for predictive chemistry. 161-169 - Carlos Outeiral, Charlotte M. Deane:
Codon language embeddings provide strong signals for use in protein engineering. 170-179 - Jens Oppliger, Michael M. Denner, Julia Küspert, Ruggero Frison, Qisi Wang, Alexander Morawietz, Oleh Ivashko, Ann-Christin Dippel, Martin von Zimmermann, Izabela Bialo, Leonardo Martinelli, Benoît Fauqué, Jaewon Choi, Mirian Garcia-Fernandez, Ke-Jin Zhou, Niels Bech Christensen, Tohru Kurosawa, Naoki Momono, Migaku Oda, Fabian D. Natterer, Mark H. Fischer, Titus Neupert, Johan Chang:
Weak signal extraction enabled by deep neural network denoising of diffraction data. 180-186 - Benjamin Q. Huynh, Elizabeth T. Chin, Allison Koenecke, Derek Ouyang, Daniel E. Ho, Mathew V. Kiang, David H. Rehkopf:
Mitigating allocative tradeoffs and harms in an environmental justice data tool. 187-194 - Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Animashree Anandkumar:
State-specific protein-ligand complex structure prediction with a multiscale deep generative model. 195-208 - Maxat Kulmanov, Francisco J. Guzmán-Vega, Paula Duek Roggli, Lydie Lane, Stefan T. Arold, Robert Hoehndorf:
Protein function prediction as approximate semantic entailment. 220-228 - Harry Coppock, George Nicholson, Ivan Kiskin, Vasiliki Koutra, Kieran Baker, Jobie Budd, Richard Payne, Emma Karoune, David Hurley, Alexander Titcomb, Sabrina Egglestone, Ana Tendero Cañadas, Lorraine Butler, Radka Jersakova, Jonathon Mellor, Selina Patel, Tracey Thornley, Peter Diggle, Sylvia Richardson, Josef Packham, Björn W. Schuller, Davide Pigoli, Steven G. Gilmour, Stephen J. Roberts, Christopher C. Holmes:
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. 229-242 - Kenny Schlegel, Denis Kleyko, Benjamin H. Brinkmann, Ewan S. Nurse, Ross W. Gayler, Peer Neubert:
Lessons from a challenge on forecasting epileptic seizures from non-cerebral signals. 243-244
Volume 6, Number 3, 2024
- The new NeuroAI. 245
- Eva Erman, Markus Furendal:
The democratization of global AI governance and the role of tech companies. 246-248 - Glen M. Hocky:
Connecting molecular properties with plain language. 249-250 - Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan P. How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa S. Parekh, Kaushik Roy, Shahaf S. Shperberg, Hava T. Siegelmann, Peter Stone, Kyle Vedder, Jingfeng Wu, Lin Yang, Guangyao Zheng, Soheil Kolouri:
A collective AI via lifelong learning and sharing at the edge. 251-264 - A. Emin Orhan, Brenden M. Lake:
Learning high-level visual representations from a child's perspective without strong inductive biases. 271-283 - Yuhe Zhang, Tobias Ritschel, Pablo Villanueva-Perez:
Reusability report: Unpaired deep-learning approaches for holographic image reconstruction. 284-290 - Rodrigo Bonazzola, Enzo Ferrante, Nishant Ravikumar, Yan Xia, Bernard D. Keavney, Sven Plein, Tanveer F. Syeda-Mahmood, Alejandro F. Frangi:
Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology. 291-306 - Yingying Cao, Tian-Gen Chang, Sahil Sahni, Eytan Ruppin:
Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data. 307-314 - Tian Lan, Shuquan Su, Pengyao Ping, Gyorgy Hutvagner, Tao Liu, Yi Pan, Jinyan Li:
Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning. 315-325 - Yuanyuan Jiang, Guo Zhang, Jing You, Hailin Zhang, Rui Yao, Huan-Zhang Xie, Liyun Zhang, Ziyi Xia, Mengzhe Dai, Yunjie Wu, Linli Li, Sheng-Yong Yang:
PocketFlow is a data-and-knowledge-driven structure-based molecular generative model. 326-337 - Kyle Swanson, Gary Liu, Denise B. Catacutan, Autumn Arnold, James Zou, Jonathan M. Stokes:
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. 338-353 - Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia:
Author Correction: A challenge for rounded evaluation of recommender systems. 368
Volume 6, Number 4, 2024
- The rewards of reusable machine learning code. 369
- Marieke Ar Bak, Vince I. Madai, Leo Anthony Celi, Georgios Kaissis, Ronald Cornet, Menno Maris, Daniel Rueckert, Alena Buyx, Stuart McLennan:
Federated learning is not a cure-all for data ethics. 370-372 - Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb:
The curious case of the test set AUROC. 373-376 - Mike H. M. Teodorescu, Mingang K. Geiger, Lily Morse:
Dangers of speech technology for workplace diversity. 377-380 - Justin N. Wood:
Artificial intelligence tackles the nature-nurture debate. 381-382 - Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, Scott A. Hale:
The benefits, risks and bounds of personalizing the alignment of large language models to individuals. 383-392 - Tianyi Li, Luca Biferale, Fabio Bonaccorso, Martino Andrea Scarpolini, Michele Buzzicotti:
Synthetic Lagrangian turbulence by generative diffusion models. 393-403 - Adamo Young, Hannes L. Röst, Bo Wang:
Tandem mass spectrum prediction for small molecules using graph transformers. 404-416 - Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael M. Bronstein, Bruno E. Correia:
Equivariant 3D-conditional diffusion model for molecular linker design. 417-427 - Taoyong Cui, Chenyu Tang, Mao Su, Shufei Zhang, Yuqiang Li, Lei Bai, Yuhan Dong, Xingao Gong, Wanli Ouyang:
Geometry-enhanced pretraining on interatomic potentials. 428-436 - Michael A. Skinnider:
Invalid SMILES are beneficial rather than detrimental to chemical language models. 437-448 - Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang:
A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions. 449-460 - Tao Xu, Haoyuan Shi, Wanling Gao, Xiaosong Wang, Zhenyu Yue:
Reusability report: Uncovering associations in biomedical bipartite networks via a bilinear attention network with domain adaptation. 461-466 - Xupeng Chen, Ran Wang, Amirhossein Khalilian-Gourtani, Leyao Yu, Patricia Dugan, Daniel Friedman, Werner K. Doyle, Orrin Devinsky, Yao Wang, Adeen Flinker:
A neural speech decoding framework leveraging deep learning and speech synthesis. 467-480 - Johannes Kühn, Tingli Hu, Alexander Toedtheide, Edmundo Pozo Fortunic, Elisabeth Rose Jensen, Sami Haddadin:
The synergy complement control approach for seamless limb-driven prostheses. 481-492 - Dylan S. Shah, Joshua P. Powers, Liana G. Tilton, Sam Kriegman, Josh C. Bongard, Rebecca Kramer-Bottiglio:
Author Correction: A soft robot that adapts to environments through shape change. 493 - Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb:
Publisher Correction: The curious case of the test set AUROC. 494
Volume 6, Number 5, 2024
- Empathic AI can't get under the skin. 495
- Garriy Shteynberg, Jodi Halpern, Amir Sadovnik, Jon Garthoff, Anat Perry, Jessica Hay, Carlos Montemayor, Michael A. Olson, Tim L. Hulsey, Abrol Fairweather:
Does it matter if empathic AI has no empathy? 496-497 - Diego Marcondes, Adilson Simonis, Junior Barrera:
Back to basics to open the black box. 498-501 - Ge Wang:
Diving into deep learning. 502-503 - Thomas A. Berrueta, Allison Pinosky, Todd D. Murphey:
Maximum diffusion reinforcement learning. 504-514 - Florian Fürrutter, Gorka Muñoz-Gil, Hans J. Briegel:
Quantum circuit synthesis with diffusion models. 515-524 - Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, Philippe Schwaller:
Augmenting large language models with chemistry tools. 525-535 - Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang:
Accurate and robust protein sequence design with CarbonDesign. 536-547 - Ning Wang, Jiang Bian, Yuchen Li, Xuhong Li, Shahid Mumtaz, Linghe Kong, Haoyi Xiong:
Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning. 548-557 - Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu:
Predicting equilibrium distributions for molecular systems with deep learning. 558-567 - Abdourahmane Diaw, Michael M. McKerns, Irina Sagert, L. G. Stanton, Michael S. Murillo:
Efficient learning of accurate surrogates for simulations of complex systems. 568-577
Volume 6, Number 6, 2024
- Will generative AI transform robotics? 579
- Mayank Kejriwal, Eric J. Kildebeck, Robert J. Steininger, Abhinav Shrivastava:
Challenges, evaluation and opportunities for open-world learning. 580-588 - Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell:
Machine learning-aided generative molecular design. 589-604 - Lidong Yang, Jialin Jiang, Fengtong Ji, Yangmin Li, Kai-Leung Yung, Antoine Ferreira, Li Zhang:
Machine learning for micro- and nanorobots. 605-618 - Kevin Max, Laura Kriener, Garibaldi Pineda García, Thomas Nowotny, Ismael Jaras, Walter Senn, Mihai A. Petrovici:
Learning efficient backprojections across cortical hierarchies in real time. 619-630 - Qianying Cao, Somdatta Goswami, George Em Karniadakis:
Laplace neural operator for solving differential equations. 631-640 - Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A. Olshausen, Yulia Sandamirskaya, Friedrich T. Sommer, Edward Paxon Frady:
Neuromorphic visual scene understanding with resonator networks. 641-652 - Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Edward Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya:
Visual odometry with neuromorphic resonator networks. 653-663 - Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar:
Distributed constrained combinatorial optimization leveraging hypergraph neural networks. 664-672 - Huan Yee Koh, Anh T. N. Nguyen, Shirui Pan, Lauren T. May, Geoffrey I. Webb:
Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data. 673-687 - Duanhua Cao, Geng Chen, Jiaxin Jiang, Jie Yu, Runze Zhang, Mingan Chen, Wei Zhang, Lifan Chen, Feisheng Zhong, Yingying Zhang, Chenghao Lu, Xutong Li, Xiaomin Luo, Sulin Zhang, Mingyue Zheng:
Generic protein-ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling. 688-700 - Evan Seitz, David M. McCandlish, Justin B. Kinney, Peter K. Koo:
Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models. 701-713 - Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji:
Coordinate-based neural representations for computational adaptive optics in widefield microscopy. 714-725 - Chenguang Li, Gabriel Kreiman, Sharad Ramanathan:
Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks. 726-738
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