Author

Mathilde Jalving

University Medical Center GroningenORCID 0000-0002-9142-9050Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:gene regulatory network inference· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Arkajyoti BhattacharyaiD· 2 shared Rudolf S.N. FehrmanniD· 2 shared Steven de JongiD· 2 shared Anna K.L. ReynersiD· 1 shared Arne van HoeckiD· 1 shared Carlos G. Urzúa-TraslaviñaiD· 1 shared Chris J. de WitteiD· 1 shared Christa van Schaik-van de Mheen· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersCancer Biology

Arkajyoti BhattacharyaORCID

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Author profile →

3 papers

Recent·Most cited

Transcriptional pattern enriched for synaptic signaling is associated with shorter survival of patients with high-grade serous ovarian cancer

Arkajyoti Bhattacharya, Thijs S Stutvoet, Mirela Perla, Stefan Loipfinger, Mathilde Jalving, Anna K.L. Reyners, Paola Drapkin Vermeer, Ronny Drapkin, Marco de Bruyn, Elisabeth G.E. de Vries, Steven de Jong, Rudolf S.N. Fehrmann

eLife · May 13, 2025Peer-reviewedCancer Biology

Distinct genomic profiles are associated with treatment response and survival in ovarian cancer

Chris J. de Witte, Joachim Kutzera, Arne van Hoeck, Luan Nguyen, Ingrid A Boere, Mathilde Jalving, Petronella Beatrix Ottevanger, Christa van Schaik-van de Mheen, Marion Stevense, Wigard P. Kloosterman, Ronald Peter Zweemer, Edwin P. J. G. Cuppen, Petronella Oda Witteveen

This study identifies genomic subgroups in ovarian cancer associated with distinct treatment responses and survival outcomes using whole genome sequencing data.

Cancers · Mar 15, 2022Peer-reviewedGenetic & Genomic Medicine

Robust and independent metabolic transcriptional components in 34,494 patient-derived samples and cell lines: a resource for translational cancer research

Vincent Christiaan Leeuwenburgh, Carlos G. Urzúa-Traslaviña, Arkajyoti Bhattacharya, Marthe T. C. Walvoort, Mathilde Jalving, Steven de Jong, Rudolf S.N. Fehrmann

The paper uses c-ICA on 34,494 bulk expression profiles to define metabolic transcriptional components and their associations with drug sensitivity, patient outcomes, and immune microenvironment.

bioRxiv · Oct 1, 2020 · PreprintBioinformatics