Geoffrey I. Webb

30.0k total citations · 10 hit papers
252 papers, 14.1k citations indexed

About

Geoffrey I. Webb is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Geoffrey I. Webb has authored 252 papers receiving a total of 14.1k indexed citations (citations by other indexed papers that have themselves been cited), including 103 papers in Artificial Intelligence, 65 papers in Molecular Biology and 49 papers in Information Systems. Recurrent topics in Geoffrey I. Webb's work include Data Mining Algorithms and Applications (46 papers), Machine Learning in Bioinformatics (42 papers) and Machine Learning and Data Classification (33 papers). Geoffrey I. Webb is often cited by papers focused on Data Mining Algorithms and Applications (46 papers), Machine Learning in Bioinformatics (42 papers) and Machine Learning and Data Classification (33 papers). Geoffrey I. Webb collaborates with scholars based in Australia, China and United States. Geoffrey I. Webb's co-authors include Claude Sammut, Jiangning Song, Fuyi Li, Tatsuya Akutsu, François Petitjean, Janice R. Boughton, André Leier, Zhihai Wang, Tatiana T. Marquez‐Lago and Kuo‐Chen Chou and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Geoffrey I. Webb

243 papers receiving 13.6k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Encyclopedia of Machine Learning

2010 2.6k citations Geoffrey I. Webb et al. profile →
Encyclopedia of Machine Learning and Data Mining

2017 633 citations Geoffrey I. Webb et al. profile →
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

2018 516 citations Fuyi Li, André Leier et al. Bioinformatics profile →
Not So Naive Bayes: Aggregating One-Dependence Estimators

2005 460 citations Geoffrey I. Webb et al. Machine Learning profile →
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data

2019 323 citations Fuyi Li, Tatiana T. Marquez‐Lago et al. Briefings in Bioinformatics profile →
Characterizing concept drift

2016 300 citations Geoffrey I. Webb, François Petitjean et al. Data Mining and Knowledge Discovery profile →
A novel selective naïve Bayes algorithm

2019 259 citations Geoffrey I. Webb et al. profile →
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

2024 139 citations Huan Yee Koh, Qingsong Wen et al. profile →
Deep Learning for Time Series Anomaly Detection: A Survey

2024 94 citations Zahra Zamanzadeh Darban, Geoffrey I. Webb et al. profile →
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

2024 80 citations Chang Wei Tan, Geoffrey I. Webb et al. profile →

Peers

Geoffrey I. Webb

CTM

Tom Fawcett United States

David Heckerman United States

Alexander Gordon United States

Mark Hall United Kingdom

Peter Reutemann New Zealand

Dan Geiger Israel

Charles Elkan United States

Pedro Larrañaga Spain

Sanghamitra Bandyopadhyay India

Janez Demšar Slovenia

Tom Fawcett United States

Geoffrey I. Webb

7.1k ×1.3

2.1k ×0.5

2.4k ×1.1

1.0k ×0.7

CTM

1.4k ×0.9

Citations per year, relative to Geoffrey I. Webb Geoffrey I. Webb (= 1×) peers Tom Fawcett

Countries citing papers authored by Geoffrey I. Webb

Since Specialization

Citations

This map shows the geographic impact of Geoffrey I. Webb's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Geoffrey I. Webb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geoffrey I. Webb more than expected).

Fields of papers citing papers by Geoffrey I. Webb

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Geoffrey I. Webb. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Geoffrey I. Webb. The network helps show where Geoffrey I. Webb may publish in the future.

Co-authorship network of co-authors of Geoffrey I. Webb

This figure shows the co-authorship network connecting the top 25 collaborators of Geoffrey I. Webb. A scholar is included among the top collaborators of Geoffrey I. Webb based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Geoffrey I. Webb. Geoffrey I. Webb is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Webb, Geoffrey I., et al.. (2026). “Accessibility misrepresentation” in public transport. Journal of Transport Geography. 133. 104632–104632.

Tan, Chang Wei, et al.. (2025). Proximity forest 2.0: a new effective and scalable similarity-based classifier for time series. Data Mining and Knowledge Discovery. 39(2).

Darban, Zahra Zamanzadeh, Geoffrey I. Webb, Charų C. Aggarwal, et al.. (2025). DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series. IEEE Transactions on Knowledge and Data Engineering. 37(8). 4485–4496. 4 indexed citations

Zheng, Yizhen, et al.. (2025). Large language models for scientific discovery in molecular property prediction. Nature Machine Intelligence. 7(3). 437–447. 14 indexed citations

Koh, Huan Yee, Thi Nguyen, Shirui Pan, Lauren T. May, & Geoffrey I. Webb. (2024). Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data. Nature Machine Intelligence. 6(6). 673–687. 29 indexed citations

Bagnall, Anthony, Matthew Middlehurst, Germain Forestier, et al.. (2024). A Hands-on Introduction to Time Series Classification and Regression. ePrints Soton (University of Southampton). 6410–6411. 2 indexed citations

Schmidt, Daniel F., et al.. (2023). Hydra: competing convolutional kernels for fast and accurate time series classification. Data Mining and Knowledge Discovery. 37(5). 1779–1805. 36 indexed citations

Webb, Geoffrey I., et al.. (2023). SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting. Machine Learning. 112(7). 2555–2591. 7 indexed citations

Fawaz, Hassan Ismail, Maxime Devanne, Jonathan Weber, et al.. (2023). Time series adversarial attacks: an investigation of smooth perturbations and defense approaches. International Journal of Data Science and Analytics. 19(1). 129–139. 2 indexed citations

10.

Nguyen, Thi, Diep Thi Ngoc Nguyen, Huan Yee Koh, et al.. (2023). The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery. British Journal of Pharmacology. 181(14). 2371–2384. 20 indexed citations

11.

Li, Chen, Yue Bi, Zhikang Wang, et al.. (2023). PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships. Bioinformatics. 39(3). 27 indexed citations

12.

Maćešić, Nenad, et al.. (2023). EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes. Journal of Biomedical Informatics. 147. 104509–104509. 6 indexed citations

13.

Wang, Yanan, Nicolas Coudray, Yun Zhao, et al.. (2021). HEAL: an automated deep learning framework for cancer histopathology image analysis. Bioinformatics. 37(22). 4291–4295. 23 indexed citations

14.

Jia, Cangzhi, Fuyi Li, Chen Li, et al.. (2021). Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction. Briefings in Bioinformatics. 23(2). 20 indexed citations

15.

Li, Fuyi, André Leier, Quanzhong Liu, et al.. (2020). Procleave: Predicting Protease-Specific Substrate Cleavage Sites by Combining Sequence and Structural Information. Genomics Proteomics & Bioinformatics. 18(1). 52–64. 74 indexed citations

16.

Tan, Chang Wei, Christoph Bergmeir, François Petitjean, & Geoffrey I. Webb. (2020). Monash University, UEA, UCR Time Series Regression Archive. arXiv (Cornell University). 4 indexed citations

17.

Webb, Geoffrey I., et al.. (2019). FACTORS RELATED TO LARGER BUT FEWER WILDFIRES AND FEWER DEER IN CALIFORNIA: A GOOGLE SITES KNOWLEDGE BASE. Issues in Information Systems. 2 indexed citations

18.

Li, Fuyi, Tatiana T. Marquez‐Lago, André Leier, et al.. (2019). PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. Briefings in Bioinformatics. 21(3). 1069–1079. 33 indexed citations

19.

Song, Jiangning, Huilin Wang, Jiawei Wang, et al.. (2017). PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection. Scientific Reports. 7(1). 6862–6862. 69 indexed citations

20.

Webb, Geoffrey I.. (2008). Multi-strategy ensemble learning, ensembles of Bayesian classifiers, and the problem of false discoveries. 15–15. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact