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Katrien Verbert

As seen in: Nature, ACM - Association for Computing Machinery, Digital Biomarkers
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Articles by Katrien Verbert

GlycanAge dynamics and their relationship to SCORE2 risk change over six months: a secondary analysis of a randomized controlled trial

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By Adrijana Svensek, Lucija Gosak, Diego Rojo, Leon Kopitar, Maja Hanic, Barbara Radovani, Katrien Verbert, Zalika Klemenc-Ketis, Mateja Lorber, Gordan Lauc, Ivana Gulišija
| Nature Verified
Abstract Chronological age dominates cardiovascular (CVD) risk prediction, but glycan-based measures may better reflect biological ageing. In this secondary analysis of a six-month randomized controlled trial in Slovenia, we examined whether IgG glycosylation–derived biological age (GlycanAge) tracked short-term changes in estimated CVD risk.
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Proceedings of the 18th ACM Conference on Recommender Systems

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By Tommaso Di Noia, Thorsten Joachims, Katrien Verbert, Pasquale Lops
| ACM - Association for Computing Machinery
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Representation Debiasing of Generated Data Involving Domain Experts

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By Aditya Bhattacharya, Simone Stumpf, Katrien Verbert
| Preprints.org
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 31 May 2024 / Approved: 3 June 2024 / Online: 3 June 2024 (07:46:41 CEST) Bhattacharya, A.; Stumpf, S.; Verbert, K. Representation Debiasing of Generated Data Involving Domain Experts. Preprints 2024, 2024060050. https://doi.org/10.20944/preprints202406.0050.v1 Bhattacharya, A.; Stumpf, S.; Verbert, K. Representation Debiasing of Generated Data Involving Domain Experts. Preprints 2024, 2024060050.
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"How Good Is Your Explanation?": Towards a Standardised Evaluation Approach for Diverse XAI Methods on Multiple Dimensions of Explainability

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By Aditya Bhattacharya, Katrien Verbert
| Preprints.org
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 29 May 2024 / Approved: 29 May 2024 / Online: 29 May 2024 (12:06:10 CEST) Bhattacharya, A.; Verbert, K. "How Good Is Your Explanation?": Towards a Standardised Evaluation Approach for Diverse XAI Methods on Multiple Dimensions of Explainability. Preprints 2024, 2024051964. https://doi.org/10.20944/preprints202405.1964.v1 Bhattacharya, A.; Verbert, K.
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