25 Most-Cited Papers | Generated by AI

Home PDF

Below is a concise introduction to each of the listed papers, summarizing their key contributions and significance in their respective fields. The introductions aim to provide a brief overview while capturing the essence of each work.

  1. Deep Residual Learning for Image Recognition (2015)
    Authors: Kaiming He et al.
    Introduces ResNet, a deep convolutional neural network architecture using residual connections to address the vanishing gradient problem. It enables training of very deep networks (hundreds of layers) while achieving state-of-the-art performance on image classification tasks like ImageNet, revolutionizing computer vision.

  2. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method (2001)
    Authors: Kenneth J. Livak, Thomas D. Schmittgen
    Describes the 2−ΔΔCT method for analyzing relative gene expression from real-time quantitative PCR data. This widely used approach normalizes gene expression levels to reference genes and a calibrator sample, providing a robust, accessible framework for molecular biology research.

  3. Using Thematic Analysis in Psychology (2006)
    Authors: Virginia Braun, Victoria Clarke
    Presents thematic analysis as a flexible qualitative research method for identifying, analyzing, and reporting patterns (themes) in data. It offers a structured yet adaptable approach, widely adopted in psychology and social sciences for qualitative data interpretation.

  4. Diagnostic and Statistical Manual of Mental Disorders, DSM-5 (2013)
    Published by: American Psychiatric Association
    The DSM-5 is a comprehensive classification system for mental disorders, providing standardized diagnostic criteria for clinicians and researchers. It updates previous editions with revised categories and criteria, serving as a cornerstone for psychiatric diagnosis and research.

  5. A Short History of SHELX (2008)
    Author: George M. Sheldrick
    Chronicles the development of SHELX, a suite of programs for crystal structure determination in X-ray crystallography. It highlights SHELX’s impact on structural chemistry, emphasizing its role in automating and refining crystallographic analyses.

  6. Random Forests (2001)
    Author: Leo Breiman
    Introduces Random Forests, an ensemble machine learning method that combines multiple decision trees to improve classification and regression accuracy. Its robustness and versatility have made it a staple in data science and predictive modeling.

  7. Attention Is All You Need (2017)
    Authors: Vaswani et al.
    Proposes the Transformer, a neural network architecture relying entirely on attention mechanisms, eliminating recurrent and convolutional layers. It revolutionized natural language processing, enabling models like BERT and GPT with superior performance in tasks like translation.

  8. ImageNet Classification with Deep Convolutional Neural Networks (2012)
    Authors: Alex Krizhevsky et al.
    Presents AlexNet, a pioneering deep convolutional neural network that achieved breakthrough results in the ImageNet competition. It popularized deep learning in computer vision, demonstrating the power of GPUs and large-scale datasets.

  9. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries (2021)
    Authors: Hyuna Sung et al.
    Provides updated global cancer burden estimates for 2020, covering incidence and mortality for 36 cancer types across 185 countries. Part of the GLOBOCAN project, it informs cancer control policies and research priorities.

  10. Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries (2018)
    Authors: Freddie Bray et al.
    Offers global cancer statistics for 2018, detailing incidence and mortality rates for 36 cancers. This GLOBOCAN report serves as a critical resource for understanding cancer trends and guiding public health strategies.

  11. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)
    Authors: David Moher et al.
    Introduces the PRISMA Statement, a 27-item checklist and flow diagram for transparent reporting of systematic reviews and meta-analyses. It enhances the quality and reproducibility of evidence synthesis in health research.

  12. U-Net: Convolutional Networks for Biomedical Image Segmentation (2015)
    Authors: Olaf Ronneberger et al.
    Describes U-Net, a convolutional neural network designed for biomedical image segmentation. Its U-shaped architecture with skip connections excels at precise segmentation tasks, widely used in medical imaging.

  13. Electric Field Effect in Atomically Thin Carbon Films (2004)
    Authors: Konstantin S. Novoselov et al.
    Reports the discovery of graphene’s electric field effect, demonstrating its potential as a two-dimensional material. This seminal work, linked to the 2010 Nobel Prize in Physics, sparked widespread research into graphene’s properties and applications.

  14. Fitting Linear Mixed-Effects Models Using lme4 (2015)
    Authors: Douglas Bates et al.
    Details the lme4 package in R for fitting linear mixed-effects models, which account for both fixed and random effects. It is widely used in statistics for analyzing hierarchical and longitudinal data.

  15. Scikit-learn: Machine Learning in Python (2011)
    Authors: Fabian Pedregosa et al.
    Introduces scikit-learn, an open-source Python library for machine learning. It provides accessible tools for classification, regression, clustering, and more, becoming a standard in data science workflows.

  16. Deep Learning (2015)
    Authors: Yann LeCun, Yoshua Bengio, Geoffrey Hinton
    Reviews deep learning, emphasizing its foundations, architectures, and applications in fields like computer vision and speech recognition. Authored by pioneers, it solidified deep learning’s transformative impact on AI.

  17. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies (2003)
    Authors: Philip M. Podsakoff et al.
    Examines common method biases in behavioral research, such as response biases in surveys, and proposes statistical and procedural remedies to mitigate their impact, improving research validity.

  18. Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2 (2014)
    Authors: Michael I. Love et al.
    Presents DESeq2, a Bioconductor package for differential gene expression analysis of RNA-seq data. It improves accuracy in estimating fold changes and dispersion, widely used in genomics research.

  19. Hallmarks of Cancer: The Next Generation (2011)
    Authors: Douglas Hanahan, Robert A. Weinberg
    Updates the original “Hallmarks of Cancer,” outlining ten key characteristics of cancer, such as sustained proliferation and immune evasion. It provides a framework for understanding cancer biology and guiding therapeutic development.

  20. Measuring Inconsistency in Meta-Analyses (2003)
    Authors: Julian P. T. Higgins et al.
    Introduces methods to quantify heterogeneity in meta-analyses, including the I² statistic. It helps researchers assess inconsistency across studies, improving the reliability of meta-analytic conclusions.

  21. NIH Image to ImageJ: 25 Years of Image Analysis (2012)
    Authors: Caroline A. Schneider et al.
    Chronicles the evolution of ImageJ, an open-source image analysis software, from NIH Image. It highlights its widespread use in scientific research for processing and analyzing images across disciplines.

  22. Fiji: An Open-Source Platform for Biological-Image Analysis (2012)
    Authors: Johannes Schindelin et al.
    Introduces Fiji, an ImageJ-based platform tailored for biological image analysis. It integrates plugins and tools for advanced image processing, widely adopted in life sciences research.

  23. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews (2021)
    Authors: Matthew J. Page et al.
    Updates the PRISMA guidelines with a 27-item checklist and flow diagram to improve transparency in systematic reviews and meta-analyses, reflecting advances in research synthesis methodology.

  24. ImageNet: A Large-Scale Hierarchical Image Database (2009)
    Authors: Jia Deng et al.
    Describes ImageNet, a massive dataset with millions of labeled images across thousands of categories. It fueled advancements in computer vision, particularly deep learning, by providing a benchmark for image classification.

  25. *GPower 3: A Flexible Statistical Power Analysis Program for the Social, Behavioral, and Biomedical Sciences (2007)**
    Authors: Franz Faul et al.
    Introduces G*Power 3, a free software tool for statistical power analysis. It supports researchers in determining sample sizes and power for various statistical tests, widely used in experimental design.

These papers span diverse fields—machine learning, psychology, cancer research, crystallography, and more—each making foundational contributions that continue to shape research and practice. If you’d like a deeper dive into any specific paper, let me know!

The Nature analysis published on April 15, 2025, identifies the 25 most-cited papers of the 21st century, with three authored by University of Toronto computer scientists. Below, I provide details on these three papers and a general overview of the list, as the full list of 25 papers is not explicitly detailed in the available sources but can be partially reconstructed based on the provided information.

Papers by University of Toronto Computer Scientists

The three papers from the University of Toronto’s Department of Computer Science, as highlighted in the Nature analysis, are tied to advancements in artificial intelligence (AI), particularly deep learning. The most prominent among them is:

  1. “ImageNet Classification with Deep Convolutional Neural Networks” (2012)
    • Authors: Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton (University of Toronto)
    • Published in: Advances in Neural Information Processing Systems (NeurIPS)
    • Citation Rank: 8th on Nature’s list of the 25 most-cited papers of the 21st century
    • Summary: Known as the “AlexNet” paper, this work introduced a deep convolutional neural network that significantly advanced image classification performance on the ImageNet dataset. It demonstrated the power of multi-layered artificial neural networks, sparking the deep learning revolution. The paper’s impact is evident in its role as a foundation for modern AI applications in computer vision.
    • Citations: While exact citation counts vary by database (e.g., Web of Science, Google Scholar), it has amassed tens of thousands of citations, reflecting its transformative influence.

The other two papers authored by University of Toronto researchers are not explicitly named in the provided sources, but they are described as being closely tied to the department’s AI research legacy. Given the context, they likely involve work by Geoffrey Hinton or his collaborators, focusing on deep learning or related AI methodologies. These could include papers on neural network architectures, optimization techniques, or applications of deep learning, published in high-impact venues like Nature, NeurIPS, or IEEE conferences.

Overview of the 25 Most-Cited Papers

The Nature analysis, based on citation data from five databases (Web of Science, Scopus, OpenAlex, Dimensions, and Google Scholar), emphasizes that the most-cited papers of the 21st century often describe methods and tools rather than groundbreaking discoveries. The top 25 papers span several fields, with a strong presence of AI, research software, statistical methods, and psychology. Key points about the list include:

Limitations and Notes

How to Access the Full List

To obtain the complete list of the 25 most-cited papers, you can refer to the original Nature article published on April 15, 2025, titled “Exclusive: the most-cited papers of the twenty-first century” (DOI: 10.1038/d41586-025-01125-9). The Supplementary Information accompanying the article contains the detailed list. Alternatively, checking databases like Google Scholar or Web of Science for highly cited AI papers from 2000 onward can provide further insights.

If you’d like me to search for additional details about the two unnamed University of Toronto papers or to generate a chart visualizing citation trends for the known papers, please let me know!

Back 2025.06.08 Donate