Nature Computational Science, Volume 4 (original) (raw)

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Volume 4, Number 1, 2024

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    Cover runners-up of 2023. 1
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    Alexis Palmer, Noah A. Smith, Arthur Spirling:
    Using proprietary language models in academic research requires explicit justification. 2-3
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    Fernando Chirigati:
    Mining the chemical space for new antibiotics. 4
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    Daniel T. O'Brien:
    Disentangling truth from bias in naturally occurring data. 5-6
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    A transformer method that predicts human lives from sequences of life events. 7-8
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    Learning physical laws from observations of complex dynamics. 9-10
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    Roger G. Melko, Juan Carrasquilla:
    Language models for quantum simulation. 11-18
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    Zachary Friedenberger, Richard Naud:
    Dendritic excitability controls overdispersion. 19-28
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    Junbo Shen, Qinze Yu, Shenyang Chen, Qingxiong Tan, Jingchen Li, Yu Li:
    Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model. 29-42
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    Germans Savcisens, Tina Eliassi-Rad, Lars Kai Hansen, Laust Hvas Mortensen, Lau Lilleholt, Anna Rogers, Ingo Zettler, Sune Lehmann:
    Using sequences of life-events to predict human lives. 43-56
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    Zhi Liu, Uma Bhandaram, Nikhil Garg:
    Quantifying spatial under-reporting disparities in resident crowdsourcing. 57-65
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    Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eléonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li:
    Constructing custom thermodynamics using deep learning. 66-85

Volume 4, Number 2, 2024

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    A look into our non-primary formats. 87-88
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    Yang Cao, Alán Aspuru-Guzik:
    Accelerating discovery in organic redox flow batteries. 89-91
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    Joseph Bakarji:
    Distilling data into code. 92-93
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    Joshua Weinstein:
    Refining the lens of DNA microscopy. 94-95
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    Chloe Markey, Samuel Croset, Olivia Ruth Woolley, Can Martin Buldun, Christian Koch, Daniel Koller, Daniel Reker:
    Characterizing emerging companies in computational drug development. 96-103
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    Téo Lemane, Nolan Lezzoche, Julien Lecubin, Eric Pelletier, Magali Lescot, Rayan Chikhi, Pierre Peterlongo:
    Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA. 104-109
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    Paul J. Blazek, Kesavan Venkatesh, Milo M. Lin:
    Automated discovery of algorithms from data. 110-118
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    Alexander M. Kloosterman, Igor Baars, Björn Högberg:
    An error correction strategy for image reconstruction by DNA sequencing microscopy. 119-127
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    Wenjie Sun, Meghan Perkins, Mathilde Huyghe, Marisa M. Faraldo, Silvia Fre, Leïla Perié, Anne-Marie Lyne:
    Extracting, filtering and simulating cellular barcodes using CellBarcode tools. 128-143

Volume 4, Number 3, 2024

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    The increasing potential and challenges of digital twins. 145-146
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    Karen Willcox, Brittany Segundo:
    The role of computational science in digital twins. 147-149
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    Luís M. A. Bettencourt:
    Recent achievements and conceptual challenges for urban digital twins. 150-153
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    Peter Bauer, Torsten Hoefler, Bjorn Stevens, Wilco Hazeleger:
    Digital twins of Earth and the computing challenge of human interaction. 154-157
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    Jie Pan:
    Materials informatics heralds a collaborative future. 158-160
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    Gokay Avci, Kim E. Jelfs:
    Enhancing discovery of host-guest binders. 161-162
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    Andreas W. Hauser:
    Pushing the limits of OFDFT with neural networks. 163-164
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    Hod Dana:
    Accelerating protein sensor optimization with machine learning. 165-166
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    Enabling comparative gene regulatory network analysis on single-cell data with SCORPION. 167-168
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    Fei Tao, He Zhang, Chenyuan Zhang:
    Advancements and challenges of digital twins in industry. 169-177
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    Alberto Ferrari, Karen Willcox:
    Digital twins in mechanical and aerospace engineering. 178-183
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    Reinhard C. Laubenbacher, Borna Mehrad, Ilya Shmulevich, Natalia A. Trayanova:
    Digital twins in medicine. 184-191
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    Michael Batty:
    Digital twins in city planning. 192-199
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    Juan Manuel Parrilla Gutierrez, Jaroslaw M. Granda, Jean-François Ayme, Michal D. Bajczyk, Liam Wilbraham, Leroy Cronin:
    Electron density-based GPT for optimization and suggestion of host-guest binders. 200-209
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    He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao:
    Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning. 210-223
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    Sarah J. Wait, Marc Expòsit, Sophia Lin, Michael Rappleye, Justin Daho Lee, Samuel A. Colby, Lily Torp, Anthony Asencio, Annette Smith, Michael Regnier, Farid Moussavi-Harami, David Baker, Christina K. Kim, Andre Berndt:
    Machine learning-guided engineering of genetically encoded fluorescent calcium indicators. 224-236
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    Daniel Osório, Anna Capasso, S. Gail Eckhardt, Uma Giri, Alexander Somma, Todd M. Pitts, Christopher H. Lieu, Wells A. Messersmith, Stacey M. Bagby, Harinder Singh, Jishnu Das, Nidhi Sahni, S. Stephen Yi, Marieke L. Kuijjer:
    Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data. 237-250

Volume 4, Number 4, April 2024

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    Harnessing quantum information to advance computing. 251-252
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    Hussam Mahmoud:
    Leveraging epidemic network models towards wildfire resilience. 253-256
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    Valerio Capraro, Matjaz Perc:
    In search of the most cooperative network. 257-258
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    Discovering metal complexes in vast chemical spaces. 259-260
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    Annotating cell types in single-cell ATAC data via the guidance of the underlying DNA sequences. 261-262
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    Hannes Kneiding, Ainara Nova, David Balcells:
    Directional multiobjective optimization of metal complexes at the billion-system scale. 263-273
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    Anzhi Sheng, Qi Su, Long Wang, Joshua B. Plotkin:
    Strategy evolution on higher-order networks. 274-284
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    Yuansong Zeng, Mai Luo, Ningyuan Shangguan, Peiyu Shi, Junxi Feng, Jin Xu, Ken Chen, Yutong Lu, Weijiang Yu, Yuedong Yang:
    Deciphering cell types by integrating scATAC-seq data with genome sequences. 285-298
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    Steve Runser, Roman Vetter, Dagmar Iber:
    SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization. 299-309

Volume 4, Number 5, May 2024

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    Accelerating scientific progress with preprints. 311
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    Domenic P. J. Germano, James M. Osborne:
    Advancements in multicellular simulations. 312-313
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    Martijn Meeter:
    Outsourcing eureka moments to artificial intelligence. 314-315
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    Zhi Wei:
    Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics. 316-317
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    Matthew Holcomb, Stefano Forli:
    A multidimensional dataset for structure-based machine learning. 318-319
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    Shuffling haplotypes to share reference panels for imputation. 320-321
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    Jiahao Xie, Yansong Zhou, Muhammad Faizan, Zewei Li, Tianshu Li, Yuhao Fu, Xinjiang Wang, Lijun Zhang:
    Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies. 322-333
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    Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun, Zongkai Yang:
    Automated discovery of symbolic laws governing skill acquisition from naturally occurring data. 334-345
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    Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang:
    Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity. 346-359
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    Théo Cavinato, Simone Rubinacci, Anna-Sapfo Malaspinas, Olivier Delaneau:
    A resampling-based approach to share reference panels. 360-366
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    Till Siebenmorgen, Filipe Menezes, Sabrina Benassou, Erinc Merdivan, Kieran Didi, André Santos Dias Mourão, Radoslaw Kitel, Pietro Liò, Stefan Kesselheim, Marie Piraud, Fabian J. Theis, Michael Sattler, Grzegorz M. Popowicz:
    MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery. 367-378
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    Steve Runser, Roman Vetter, Dagmar Iber:
    Publisher Correction: SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization. 379

Volume 4, Number 6, June 2024

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    An invitation to social scientists. 381
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    Mayank Kejriwal, Victoria Petryshyn:
    Advancing computational sustainability in higher education. 382-383
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    Chiheb Ben Mahmoud, John L. A. Gardner, Volker L. Deringer:
    Data as the next challenge in atomistic machine learning. 384-387
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    Yifan Yang, Fan Xu:
    Computational morphology and morphogenesis for empowering soft-matter engineering. 388-390
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    Ananya Rastogi:
    Integrating computational and experimental worlds. 391-392
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    Ananya Rastogi:
    The whole picture in digital pathology. 393
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    Tianyu Wang:
    A nonlinear dimension for machine learning in optical disordered media. 394-395
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    Enrico Trizio, Peilin Kang, Michele Parrinello:
    Systematic simulations and analysis of transition states using committor functions. 396-397
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    Francisco Barreras, Duncan J. Watts:
    The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling. 398-411
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    Mai Ha Vu, Philippe A. Robert, Rahmad Akbar, Bartlomiej Swiatczak, Geir Kjetil Sandve, Dag Trygve Truslew Haug, Victor Greiff:
    Linguistics-based formalization of the antibody language as a basis for antibody language models. 412-422
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    Gokul Gowri, Kuanwei Sheng, Peng Yin:
    Scalable design of orthogonal DNA barcode libraries. 423-428
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    Hao Wang, Jianqi Hu, Andrea Morandi, Alfonso Nardi, Fei Xia, Xuanchen Li, Romolo Savo, Qiang Liu, Rachel Grange, Sylvain Gigan:
    Large-scale photonic computing with nonlinear disordered media. 429-439
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    Jenna C. Fromer, Connor W. Coley:
    An algorithmic framework for synthetic cost-aware decision making in molecular design. 440-450
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    Peilin Kang, Enrico Trizio, Michele Parrinello:
    Computing the committor with the committor to study the transition state ensemble. 451-460

Volume 4, Number 7, July 2024

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    150 years of oxygen chemistry. 461
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    De-en Jiang:
    Computational electrochemistry of oxygen 150 years after Priestley. 462-464
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    Alexandre Hocquet, Frédéric Wieber, Gabriele Gramelsberger, Konrad Hinsen, Markus Diesmann, Fernando Pasquini Santos, Catharina Landström, Benjamin Peters, Dawid Kasprowicz, Arianna Borrelli, Phillip Roth, Clarissa Ai Ling Lee, Alin Olteanu, Stefan Böschen:
    Software in science is ubiquitous yet overlooked. 465-468
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    Takahiro Yabe, Massimiliano Luca, Kota Tsubouchi, Bruno Lepri, Marta C. González, Esteban Moro:
    Enhancing human mobility research with open and standardized datasets. 469-472
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    Jiancheng Yang:
    Multi-task learning for medical foundation models. 473-474
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    Miaozhe Huo, Yuepeng Jiang, Shuai Cheng Li:
    Unlocking T-cell receptor-epitope insights with structural analysis. 475-476
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    Juan Manuel Restrepo-Flórez:
    Free-form metamaterials design with isotropic materials. 477-478
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    A multi-task learning strategy to pretrain models for medical image analysis. 479-480
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    Boosting graph neural networks with virtual nodes to predict phonon properties. 481-482
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    Steven L. Brunton, J. Nathan Kutz:
    Promising directions of machine learning for partial differential equations. 483-494
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    Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, Fabian Kiessling:
    Overcoming data scarcity in biomedical imaging with a foundational multi-task model. 495-509
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    Vadim K. Karnaukhov, Dmitrii S. Shcherbinin, Anton O. Chugunov, Dmitriy M. Chudakov, Roman G. Efremov, Ivan V. Zvyagin, Mikhail Shugay:
    Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen. 510-521
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    Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan C. Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, Mingda Li:
    Virtual node graph neural network for full phonon prediction. 522-531
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    Liujun Xu, Gaole Dai, Fubao Yang, Jinrong Liu, Yuhong Zhou, Jun Wang, Guoqiang Xu, Jiping Huang, Cheng-Wei Qiu:
    Free-form and multi-physical metamaterials with forward conformality-assisted tracing. 532-541

Volume 4, Number 8, August 2024

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    Metamaterials design via and for computation. 543-544
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    Yandong Li, Francesco Monticone:
    Exploring the role of metamaterials in achieving advantage in optical computing. 545-548
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    Gary P. T. Choi:
    Computational design of art-inspired metamaterials. 549-552
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    Keith A. Brown, Grace X. Gu:
    Computational challenges in additive manufacturing for metamaterials design. 553-555
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    Jie Pan:
    Synergy between photonic metamaterials and AI. 556-557
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    Jie Pan:
    Enhancing structured light with optimized metasurfaces. 558
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    Rui-Jie Zhu, Skye Gunasekaran, Jason Eshraghian:
    Bridging the gap between artificial intelligence and natural intelligence. 559-560
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    Ting Cao:
    Accelerating predictions of electronic transport and superconductivity. 561-562
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    AI-recognized mitochondrial phenotype enables identification of drug targets. 563-564
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    A machine learning tool to efficiently calculate electron-phonon coupling. 565-566
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    Qiguang He, Samuele Ferracin, Jordan R. Raney:
    Programmable responsive metamaterials for mechanical computing and robotics. 567-573
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    Silvia Bonfanti, Stefan Hiemer, Raja Zulkarnain, Roberto Guerra, Michael Zaiser, Stefano Zapperi:
    Computational design of mechanical metamaterials. 574-583
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    Linxuan He, Yunhui Xu, Weihua He, Yihan Lin, Yang Tian, Yujie Wu, Wenhui Wang, Ziyang Zhang, Junwei Han, Yonghong Tian, Bo Xu, Guoqi Li:
    Network model with internal complexity bridges artificial intelligence and neuroscience. 584-599
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    Min Yu, Weiming Li, Yunru Yu, Yu Zhao, Lizhi Xiao, Volker M. Lauschke, Yiyu Cheng, Xingcai Zhang, Yi Wang:
    Deep learning large-scale drug discovery and repurposing. 600-614
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    Yang Zhong, Shixu Liu, Binhua Zhang, Zhiguo Tao, Yuting Sun, Weibin Chu, Xin-Gao Gong, Ji-Hui Yang, Hongjun Xiang:
    Accelerating the calculation of electron-phonon coupling strength with machine learning. 615-625

Volume 4, Number 9, September 2024

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    Putting a spotlight on diversity, equity, and inclusion. 627-628
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    Sophia Chen:
    The lost data: how AI systems censor LGBTQ+ content in the name of safety. 629-632
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    Laetitia Gauvin:
    Gaps in gender and socioeconomic mobility disparity studies. 633-635
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    Vinod Namboodiri:
    Harnessing the power of emerging computational capabilities for independent mobility for persons with disabilities. 636-637
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    Elaine O. Nsoesie, Marzyeh Ghassemi:
    Using labels to limit AI misuse in health. 638-640
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    Siddharth Suri:
    Defining our future with generative AI. 641-643
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    Joaquín Barroso-Flores:
    Accelerating economic development in Latin America through overcoming access challenges to supercomputing infrastructure. 644-645
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    Christine Yifeng Chen, Alan Christoffels, Roger Dube, Kamuela Enos, Juan E. Gilbert, Sanmi Koyejo, Jason Leigh, Carlo Liquido, Amy McKee, Kari Noe, Tai-Quan Peng, Karaitiana Taiuru:
    Increasing the presence of BIPOC researchers in computational science. 646-653
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    Fernando Chirigati:
    Heat wave attribution assessment using deep learning. 654
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    Jie Hu:
    Deconstructing the compounds of altruism. 655-656
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    Yicheng Gao, Qi Liu:
    Delineating cell types with transcriptional kinetics. 657-658
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    Xiaoyan Wu, Xiangjuan Ren, Chao Liu, Hang Zhang:
    The motive cocktail in altruistic behaviors. 659-676
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    Tara Chari, Gennady Gorin, Lior Pachter:
    Biophysically interpretable inference of cell types from multimodal sequencing data. 677-689
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    Shenghao Wu, Chengcheng Huang, Adam C. Snyder, Matthew A. Smith, Brent Doiron, Byron M. Yu:
    Automated customization of large-scale spiking network models to neuronal population activity. 690-705
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    Yeganeh M. Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül:
    Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS. 706-722

Volume 4, Number 10, October 2024

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    Fostering discussions on topical issues. 723
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    Kaitlin McCardle:
    Efficient simulations of electronic spectra. 724
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    Stefan Peidli:
    The decomposition of perturbation modeling. 725-726
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    Zachary Fralish, Daniel Reker:
    Taking a deep dive with active learning for drug discovery. 727-728
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    Attila Cangi:
    Bridging the gap in electronic structure calculations via machine learning. 729-730
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    Adrian Pacheco-Pozo, Diego Krapf:
    Effectively detecting anomalous diffusion via deep learning. 731-732
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    Joint inference of discrete and continuous factors captures variability across and within cell types. 733-734
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    Guy Durant, Fergus Boyles, Kristian Birchall, Charlotte M. Deane:
    The future of machine learning for small-molecule drug discovery will be driven by data. 735-743
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    Christian Gaser, Polona Kalc, James H. Cole:
    A perspective on brain-age estimation and its clinical promise. 744-751
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    Xiaoxun Gong, Steven G. Louie, Wenhui Duan, Yong Xu:
    Generalizing deep learning electronic structure calculation to the plane-wave basis. 752-760
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    Xiaochen Feng, Hao Sha, Yongbing Zhang, Yaoquan Su, Shuai Liu, Yuan Jiang, Shangguo Hou, Sanyang Han, Xiangyang Ji:
    Reliable deep learning in anomalous diffusion against out-of-distribution dynamics. 761-772
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    Yicheng Gao, Zhiting Wei, Kejing Dong, Ke Chen, Jingya Yang, Guohui Chuai, Qi Liu:
    Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond. 773-785
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    Derek van Tilborg, Francesca Grisoni:
    Traversing chemical space with active deep learning for low-data drug discovery. 786-796
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    Christian Gaser, Polona Kalc, James H. Cole:
    Publisher Correction: A perspective on brain-age estimation and its clinical promise. 797
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    Christine Yifeng Chen, Alan Christoffels, Roger Dube, Kamuela Enos, Juan E. Gilbert, Sanmi Koyejo, Jason Leigh, Carlo Liquido, Amy McKee, Kari Noe, Tai-Quan Peng, Karaitiana Taiuru:
    Publisher Correction: Increasing the presence of BIPOC researchers in computational science. 798
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    Yeganeh M. Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül:
    Publisher Correction: Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS. 799

Volume 4, Number 11, November 2024

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    Harnessing the power of DNA for computing. 801
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  • 
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    Fernando Chirigati:
    Collective deliberation driven by AI. 802
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    Christina L. Vizcarra, Ryan F. Trainor, Ashley Ringer McDonald, Chris T. Richardson, Davit Potoyan, Jessica Nash, Britt Lundgren, Tyler Luchko, Glen M. Hocky, Jonathan J. Foley, Timothy J. Atherton, Grace Y. Stokes:
    An interdisciplinary effort to integrate coding into science courses. 803-804
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  • 
    Loïc Lannelongue:
    Modeling the increase of electronic waste due to generative AI. 805-806
• 
  • 
  • 
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  • 
    Minghui Li:
    Enhancing protein stability prediction with geometric learning and pre-training strategies. 807-808
• 
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    Orestis A. Ntintas, Theodoros Daglis, Vassilis G. Gorgoulis:
    Harnessing deep learning to build optimized ligands. 809-810
• 
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  • 
    Weikang Li, Dong-Ling Deng:
    Extracting reliable quantum outputs for noisy devices. 811-812
• 
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  • 
    Daniella Bar-Lev, Omer Sabary, Eitan Yaakobi:
    The zettabyte era is in our DNA. 813-817
• 
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  • 
    Peng Wang, Ling-Yu Zhang, Asaf Tzachor, Wei-Qiang Chen:
    E-waste challenges of generative artificial intelligence. 818-823
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  • 
    Nessim Raouraoua, Claudio Mirabello, Thibaut Véry, Christophe Blanchet, Björn Wallner, Marc F. Lensink, Guillaume Brysbaert:
    MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling. 824-828
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  • 
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  • 
    Felix Wong, Dongchen He, Aarti Krishnan, Liang Hong, Alexander Z. Wang, Jiuming Wang, Zhihang Hu, Satotaka Omori, Alicia Li, Jiahua Rao, Qinze Yu, Wengong Jin, Tianqing Zhang, Katherine Ilia, Jack X. Chen, Shuangjia Zheng, Irwin King, Yu Li, James J. Collins:
    Deep generative design of RNA aptamers using structural predictions. 829-839
• 
  • 
  • 
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  • 
    Yunxin Xu, Di Liu, Haipeng Gong:
    Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy. 840-850
• 
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  • 
    Pengyong Li, Kaihao Zhang, Tianxiao Liu, Ruiqiang Lu, Yangyang Chen, Xiaojun Yao, Lin Gao, Xiangxiang Zeng:
    A deep learning approach for rational ligand generation with toxicity control via reactive building blocks. 851-864
• 
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    Samantha V. Barron, Daniel J. Egger, Elijah Pelofske, Andreas Bärtschi, Stephan J. Eidenbenz, Matthis Lehmkuehler, Stefan Woerner:
    Provable bounds for noise-free expectation values computed from noisy samples. 865-875
• 
  • 
  • 
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  • 
    He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, Yong Xu:
    Author Correction: Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. 876
• 
  • 
  • 
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  • 
    Xiaochen Feng, Hao Sha, Yongbing Zhang, Yaoquan Su, Shuai Liu, Yuan Jiang, Shangguo Hou, Sanyang Han, Xiangyang Ji:
    Publisher Correction: Reliable deep learning in anomalous diffusion against out-of-distribution dynamics. 877

Volume 4, Number 12, December 2024

• 
  • 
  • 
  • 
  • 
    Jie Pan:
    Effective quantum error correction by AI. 881
• 
  • 
  • 
  • 
  • 
    Felix Wang, James B. Aimone:
    On the path toward brain-scale simulations. 882-883
• 
  • 
  • 
  • 
  • 
    Yongle Li, Yuhao Chen, Xiao He:
    Teaching spin symmetry while learning neural network wave functions. 884-885
• 
  • 
  • 
  • 
  • 
    Deep learning training dynamics analysis for single-cell data. 886-887
• 
  • 
  • 
  • 
  • 
    A simulated C. elegans with biophysically detailed neurons and muscle dynamics. 888-889
• 
  • 
  • 
  • 
  • 
    Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng:
    Simulation and assimilation of the digital human brain. 890-898
• 
  • 
  • 
  • 
  • 
    Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Rokkum Jamasb, Ilia Igashov, Weitao Du, Carla P. Gomes, Tom L. Blundell, Pietro Lio, Max Welling, Michael M. Bronstein, Bruno E. Correia:
    Structure-based drug design with equivariant diffusion models. 899-909
• 
  • 
  • 
  • 
  • 
    Zhe Li, Zixiang Lu, Ruichen Li, Xuelan Wen, Xiang Li, Liwei Wang, Ji Chen, Weiluo Ren:
    Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network. 910-919
• 
  • 
  • 
  • 
  • 
    Juntian Ye, Yi Sun, Wenwen Li, Jian-Wei Zeng, Yu Hong, Zheng-Ping Li, Xin Huang, Xianghui Xue, Xin Yuan, Feihu Xu, Xiankang Dou, Jian-Wei Pan:
    Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation. 920-927
• 
  • 
  • 
  • 
  • 
    Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu, Natalia A. Trayanova, Mauro Maggioni:
    A scalable framework for learning the geometry-dependent solution operators of partial differential equations. 928-940
• 
  • 
  • 
  • 
  • 
    Jonathan Karin, Reshef Mintz, Barak Raveh, Mor Nitzan:
    Interpreting single-cell and spatial omics data using deep neural network training dynamics. 941-954
• 
  • 
  • 
  • 
  • 
    Aarthi Venkat, Samuel Leone, Scott E. Youlten, Eric Fagerberg, John Attanasio, Nikhil S. Joshi, Michael Perlmutter, Smita Krishnaswamy:
    Mapping the gene space at single-cell resolution with gene signal pattern analysis. 955-977
• 
  • 
  • 
  • 
  • 
    Mengdi Zhao, Ning Wang, Xinrui Jiang, Xiaoyang Ma, Haixin Ma, Gan He, Kai Du, Lei Ma, Tiejun Huang:
    An integrative data-driven model simulating C. elegans brain, body and environment interactions. 978-990
• 
  • 
  • 
  • 
  • 
    Yeganeh M. Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül:
    Author Correction: Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS. 991

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