Twan van Laarhoven
- Biophysics top 2%
- Analytical Chemistry top 2%
- Cancer Research top 10%
- Molecular Biology top 10%
- Protein Structure and Dynamics 2
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- Complex Network Analysis Techniques 6
- Opinion Dynamics and Social Influence 5
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- Gaussian Processes and Bayesian Inference 4
- Advanced Graph Neural Networks 4
- Domain Adaptation and Few-Shot Learning 4
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- Multimodal Machine Learning Applications 3
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- COVID-19 diagnosis using AI 3
- Co-authors
- Elena MarchioriSander B. NabuursL.M.C. BuydensThanh N. TranJan GerretzenNastaran Mohammadian RadCesare FurlanelloGiuseppe Jurman
- Journals
- PLoS ONE (3 papers)Journal of Machine Learning Research (2 papers)Artificial Intelligence in Medicine (1 paper)
- Partner nations
- NetherlandsChinaItaly
In The Last Decade
Twan van Laarhoven
26 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 125
- Computational Theory and Mathematics 528
- Biophysics 150
- Analytical Chemistry 245
- Cancer Research 300
- Molecular Biology 860
Countries citing papers authored by Twan van Laarhoven
This map shows the geographic impact of Twan van Laarhoven'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 Twan van Laarhoven with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Twan van Laarhoven more than expected).
Fields of papers citing papers by Twan van Laarhoven
This network shows the impact of papers produced by Twan van Laarhoven. 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 Twan van Laarhoven. The network helps show where Twan van Laarhoven may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Twan van Laarhoven, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2026 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 1 | |
| 6 | 2022 | 10 | |
| 7 | 2021 | 18 | |
| 8 | 2021 | 4 | |
| 9 | 2020 | 7 | |
| 10 | 2019 | 5 | |
| 11 | Simple Domain Adaptation with Class Prediction Uncertainty Alignment. | 2018 | 8 |
| 12 | 2018 | 57 | |
| 13 | 2018 | 4 | |
| 14 | Convolutional Neural Networks and Data Augmentation for Spectral-Spatial Classification of Hyperspectral Images. | 2017 | 10 |
| 15 | 2016 | 24 | |
| 16 | Convolutional neural networks for vibrational spectroscopic data analysisbreakdown → | 2016 | 330 |
| 17 | 2014 | 1 | |
| 18 | Network community detection using edge classifiers trained on LFR graphs. | 2013 | 1 |
| 19 | 2013 | 191 | |
| 20 | 2013 | 10 |
About Twan van Laarhoven
Twan van Laarhoven is a scholar working on Health Informatics, Statistical and Nonlinear Physics, Artificial Intelligence, Computer Vision and Pattern Recognition and Physical Therapy, Sports Therapy and Rehabilitation, having authored 28 papers that have together received 1.5k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Opinion Dynamics and Social Influence (5 papers), Gaussian Processes and Bayesian Inference (4 papers), Advanced Graph Neural Networks (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers), COVID-19 diagnosis using AI (3 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (528 citations), Biophysics (150 citations), Analytical Chemistry (245 citations), Cancer Research (300 citations) and Molecular Biology (860 citations). Twan van Laarhoven has collaborated with scholars based in Netherlands, China and Italy. Frequent co-authors include Elena Marchiori, Sander B. Nabuurs, L.M.C. Buydens, Thanh N. Tran, Jan Gerretzen, Nastaran Mohammadian Rad, Cesare Furlanello, Giuseppe Jurman, Seyed Mostafa Kia and Paola Venuti. Their work appears in journals such as PLoS ONE, Journal of Machine Learning Research, Artificial Intelligence in Medicine, Big Data and Analytica Chimica Acta.
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.