Institution · Vector Institute
Most cited papers from Vector Institute
What are the most cited papers from Vector Institute?
The most cited paper from Vector Institute is "The repertoire of mutational signatures in human cancer" (Ludmil B. Alexandrov et al., 2020), with 3,854 citations. Next come "TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods" (3,581) and "Pan-cancer analysis of whole genomes" (3,360).
Showing 50 of 59 papers. Every paper with more than 366 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | The repertoire of mutational signatures in human cancer Ludmil B. Alexandrov et al. · Nature | 5 Feb 2020 | 3,854 |
| 2 | TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods Professor Gary S. Collins et al. · BMJ | 16 Apr 2024 | 3,581 |
| 3 | Pan-cancer analysis of whole genomes Lauri A. Aaltonen et al. · Nature | 5 Feb 2020 | 3,360 |
| 4 | Segment anything in medical images Jun Ma, Yuting He, Feifei Li et al. · Nature Communications | 22 Jan 2024 | 2,830 |
| 5 | Inverse molecular design using machine learning: Generative models for matter engineering Benjamín Sánchez-Lengeling, Alán Aspuru‐Guzik · Science | 26 Jul 2018 | 2,193 |
| 6 | Shortcut learning in deep neural networks Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis et al. · Nature Machine Intelligence | 10 Nov 2020 | 2,111 |
| 7 | Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction Ali Punjani, Haowei Zhang, David J. Fleet · Nature Methods | 30 Nov 2020 | 1,940 |
| 8 | Noisy intermediate-scale quantum algorithms Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw et al. · Reviews of Modern Physics | 15 Feb 2022 | 1,798 |
| 9 | Moment Matching for Multi-Source Domain Adaptation Xingchao Peng, Qinxun Bai, Xide Xia et al. | Oct 2019 | 1,659 |
| 10 | The false hope of current approaches to explainable artificial intelligence in health care Marzyeh Ghassemi, Luke Oakden‐Rayner, Andrew L. Beam · The Lancet Digital Health | 25 Oct 2021 | 1,643 |
| 11 | Deep learning enables rapid identification of potent DDR1 kinase inhibitors Alex A. Zhavoronkov et al. · Nature Biotechnology | Sep 2019 | 1,616 |
| 12 | scGPT: toward building a foundation model for single-cell multi-omics using generative AI Haotian Cui, Chloe Wang, Hassaan Maan et al. · Nature Methods | 26 Feb 2024 | 1,257 |
| 13 | Do no harm: a roadmap for responsible machine learning for health care Jenna Wiens, Suchi Saria, Mark Sendak et al. · Nature Medicine | 19 Aug 2019 | 1,233 |
| 14 | The evolutionary history of 2,658 cancers Moritz Gerstung et al. · Nature | 5 Feb 2020 | 1,149 |
| 15 | A deep learning framework for neuroscience Blake A. Richards et al. · Nature Neuroscience | 28 Oct 2019 | 1,123 |
| 16 | 3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM Ali Punjani, David J. Fleet · Journal of Structural Biology | 11 Feb 2021 | 1,102 |
| 17 | Patterns of somatic structural variation in human cancer genomes Yilong Li et al. · Nature | 5 Feb 2020 | 1,005 |
| 18 | Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations Laleh Seyyed-Kalantari, Haoran Zhang, Matthew B. A. McDermott et al. · Nature Medicine | Dec 2021 | 910 |
| 19 | Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms Sukin Sim, Peter D. Johnson, Alán Aspuru‐Guzik · Advanced Quantum Technologies | 14 Oct 2019 | 872 |
| 20 | Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing Isidro Cortés-Ciriano et al. · Nature Genetics | 5 Feb 2020 | 793 |
| 21 | Gated-SCNN: Gated Shape CNNs for Semantic Segmentation Towaki Takikawa, David Acuna, Varun Jampani et al. | Oct 2019 | 763 |
| 22 | Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning Lukas Brunke, Melissa Greeff, Adam W. Hall et al. · Annual Review of Control Robotics and Autonomous Systems | 26 Jan 2022 | 762 |
| 23 | AI models collapse when trained on recursively generated data Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao et al. · Nature | 24 Jul 2024 | 739 |
| 24 | Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation Mario Krenn, Florian Häse, AkshatKumar Nigam et al. · Repository KITopen (Karlsruhe Institute of Technology) | 2020 | 727 |
| 25 | Machine Unlearning Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo et al. | May 2021 | 708 |
| 26 | Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities Marinka Žitnik, Francis Ngoc Hoang Long Nguyen, Bo Wang et al. · Information Fusion | 21 Sep 2018 | 689 |
| 27 | Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models Andreas Blattmann, Robin Rombach, Huan Ling et al. | Jun 2023 | 676 |
| 28 | Analyses of non-coding somatic drivers in 2,658 cancer whole genomes Esther Rheinbay et al. · Nature | 5 Feb 2020 | 674 |
| 29 | Self-Driving Laboratories for Chemistry and Materials Science Gary Tom, Stefan P. Schmid, Sterling G. Baird et al. · Chemical Reviews | 13 Aug 2024 | 643 |
| 30 | Advances in high-dimensional quantum entanglement Manuel Erhard, Mario Krenn, Anton Zeilinger · Nature Reviews Physics | 25 Jun 2020 | 601 |
| 31 | Machine-learned potentials for next-generation matter simulations Pascal Friederich, Florian Häse, Jonny Proppe et al. · Nature Materials | 27 May 2021 | 589 |
| 32 | Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes Stefan C. Dentro et al. · Cell | Apr 2021 | 577 |
| 33 | AI recognition of patient race in medical imaging: a modelling study Judy Wawira Gichoya et al. · The Lancet Digital Health | 11 May 2022 | 567 |
| 34 | Neural Ordinary Differential Equations Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt et al. · arXiv (Cornell University) | 19 Jun 2018 | 567 |
| 35 | Nanoparticle synthesis assisted by machine learning Huachen Tao, Tianyi Wu, Matteo Aldeghi et al. · Nature Reviews Materials | 13 Jul 2021 | 548 |
| 36 | Ethical Machine Learning in Healthcare Irene Y. Chen, Emma Pierson, Sherri Rose et al. · Annual Review of Biomedical Data Science | 6 May 2021 | 546 |
| 37 | Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery Andrew Rosen, Shaelyn M. Iyer, Debmalya Ray et al. · Matter | 5 Apr 2021 | 540 |
| 38 | Transparency and reproducibility in artificial intelligence Benjamin Haibe‐Kains et al. · Nature | 14 Oct 2020 | 531 |
| 39 | Do as AI say: susceptibility in deployment of clinical decision-aids Susanne Gaube, Harini Suresh, Martina Raue et al. · npj Digital Medicine | 19 Feb 2021 | 528 |
| 40 | Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy Sumaiyah K. Rehman et al. · Cell | 2021 | 520 |
| 41 | Machine learning for a sustainable energy future Zhenpeng Yao, Yanwei Lum, Andrew K. Johnston et al. · Nature Reviews Materials | 18 Oct 2022 | 518 |
| 42 | MLPerf Inference Benchmark Vijay Janapa Reddi et al. | May 2020 | 517 |
| 43 | Data-Driven Strategies for Accelerated Materials Design Robert Pollice, Gabe Gomes, Matteo Aldeghi et al. · Accounts of Chemical Research | 2 Feb 2021 | 505 |
| 44 | Metrics reloaded: recommendations for image analysis validation Lena Maier‐Hein et al. · Nature Methods | Feb 2024 | 467 |
| 45 | Towards Diverse and Natural Image Descriptions via a Conditional GAN Bo Dai, Sanja Fidler, Raquel Urtasun et al. | Oct 2017 | 465 |
| 46 | Machine learning approaches to drug response prediction: challenges and recent progress George Alexandru Adam, Ladislav Rampášek, Zhaleh Safikhani et al. · npj Precision Oncology | 15 Jun 2020 | 457 |
| 47 | Next-Generation Experimentation with Self-Driving Laboratories Florian Häse, Loı̈c M. Roch, Alán Aspuru‐Guzik · Trends in Chemistry | 6 Mar 2019 | 442 |
| 48 | Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes Towaki Takikawa, Joey Litalien, Kangxue Yin et al. | Jun 2021 | 437 |
| 49 | MLCM: Multi-Label Confusion Matrix Mohammadreza Heydarian, Thomas E. Doyle, Reza Samavi · IEEE Access | 2022 | 430 |
| 50 | Genomic basis for RNA alterations in cancer PCAWG Transcriptome Core Group et al. · Nature | 5 Feb 2020 | 425 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.