Profile
Statistics

Olivier Gevaert

Bio

The Gevaert lab focuses on biomedical data fusion in oncology: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Previously I pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. My subsequent work concerned the development of methods for multi-omics data fusion. This resulted in the development of MethylMix, to identify differentially methylation driven genes, and AMARETTO, a computational method to integrate DNA methylation, copy number and gene expression data to identify cancer modules. Additionally, my lab focuses on multi-scale data fusion by linking omics with cellular and tissue-level phenotypes. This led to key contributions in the field of imaging genomics/radiogenomics involving work in lung cancer and brain tumors. Our work in imaging genomics is focused on developing a framework for non-invasive personalized medicine. In summary, my lab has an interdisciplinary focus on developing novel algorithms for multi-scale biomedical data fusion.

Research Fields

Biomedical Engineering

Editorial Board Memberships

Olivier is not currently contributing as an editor for any journal or publisher.

Pre Publication Reviews

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Reviews

95

Reviews (last 12 months)

26

Reviews (average per year)

15

Merit

315

Openness

6.3%

Review to Publication ratio

2:1


Journal Impact Factors of journals reviewed for

The distribution of the Journal Impact Factors of journals Olivier Gevaert has reviewed for.

Olivier Gevaert

All fields reviewers

Total reviews over time

A cumulative record of Olivier Gevaert's total number of reviews.

Reviews per month

The total number of reviews performed by Olivier Gevaert each month.

Average review length

The average number of words per review compared to the average of All fields reviewers and the average of reviewers at affiliated institutions.

Note: only reviews with associated content are included in the graph below and where content is present we can not guarantee that it is complete or truly the content of the review.

Weekly review punchcard

The distribution of days that reviews were performed on, compared to All fields reviewers and reviewers at affiliated institutions.