Thibault Helleputte

973 citations
16 papers · 642 · h-index 8

Impact in

Papers in

    • Gene expression and cancer classification 5
    • Machine Learning in Bioinformatics 3
    • vaccines and immunoinformatics approaches 2
    • Pancreatic function and diabetes 1

Thibault Helleputte

15 papers receiving 630 citations

Peers

Thibault Helleputte
Comparison fields: 5 of 112
  • Health Informatics 22
  • Health Information Management 32
  • Artificial Intelligence 181
  • Molecular Biology 328
  • Computer Vision and Pattern Recognition 94
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Citations per year

Countries citing papers authored by Thibault Helleputte

Since Specialization
Citations

This map shows the geographic impact of Thibault Helleputte'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 Thibault Helleputte with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thibault Helleputte more than expected).

Fields of papers citing papers by Thibault Helleputte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thibault Helleputte. 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 Thibault Helleputte. The network helps show where Thibault Helleputte may publish in the future.

Co-authors

The 25 scholars most cited alongside Thibault Helleputte, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Thibault Helleputte Line = papers co-authored together Thibault Helleputte links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2009411
2 201975
3 201231
4 201529
5 201528
6 200923
7 201414
8
Clinical response to the MAGE-A3 immunotherapeutic in metastatic melanoma patients is associated with a specific gene profile present prior to treatment
20088
9 20237
10 20196
11 20184
12
Machine learning in the biopharma industry.
20202
13
Robust biomarker identification for cancer diagnosis using ensemble feature selection methods
20092
14 20251
15
Biomarker selection by transfer learning with linear regularized models
20091
16 20250

About Thibault Helleputte

Thibault Helleputte is a scholar working on Molecular Biology, Surgery, Artificial Intelligence, Computer Vision and Pattern Recognition and Epidemiology, having authored 16 papers that have together received 642 indexed citations. Recurring topics across this work include Gene expression and cancer classification (5 papers), Machine Learning in Bioinformatics (3 papers), Face and Expression Recognition (2 papers), vaccines and immunoinformatics approaches (2 papers), Liver Disease and Transplantation (2 papers), Pancreatic function and diabetes (1 paper), Diabetes Management and Research (1 paper) and Allergic Rhinitis and Sensitization (1 paper). The work is most often cited by research in Health Informatics (22 citations), Health Information Management (32 citations), Artificial Intelligence (181 citations), Molecular Biology (328 citations) and Computer Vision and Pattern Recognition (94 citations). Thibault Helleputte has collaborated with scholars based in Belgium, United States and Switzerland. Frequent co-authors include Pierre Dupont, Yvan Saeys, Thomas Abeel, Yves Van de Peer, Damien Gruson, Xavier Stéphenne, Françoise Smets, Étienne Sokal, Catherine Wanty and Bertrand Bearzatto. Their work appears in journals such as PLoS ONE, Allergy, Science Translational Medicine, International Journal of Clinical Practice and Oncotarget.

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.

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