Olivier Gevaert
Academic Appointments
- Research Associate, Radiology - Diagnostic Radiology
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Professional Overview
Professional Interests
The long-term aim of my research program is to exploit the synergies that are present between data at these different scales. This includes molecular data (e.g., genome sequencing, gene expression), cellular data (e.g., histological images) and data that characterize a disease at the tissue scale (e.g., in vivo CT or MR images) where relevant for a particular disease. By fusing molecular information with traditional phenotypic information, the high dimensionality of molecular data can be countered by focusing on correlations with relevant phenotypes. Moreover, data fusion creates the possibility to investigate how form follows molecular function at both the cellular and tissue scale.
The main objectives of my research program are:
1.To develop methods for data fusion of biomedical data
2.To develop methods that harness computational derived features extracted from images or other sources of non-molecular phenotypic information
3.To develop decision-support models for predicting diagnosis, prognosis and therapy response
Education and Certifications
| Certificate: | Stanford Business School, Stanford Ignite (2012) |
| Ph.D: | University of Leuven, Belgium, BIoinformatics (2008) |
| M.S.: | University of Leuven, Belgium, Artificial Intelligence (2004) |
| M.S.: | University College, Ghent, Belgium, Electrical Engineering/Computer Science (2003) |
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Honors and Awards
- Honorary Fellow, Belgian American Educational Foundation (BAEF) (06-01-2009)
- Henri Benedictus Fellow, King Baudouin Foundation (06-01-2009)
- Honorary Fulbright Scholar, Commission for Educational Exchange between the United States of America, Belgium and Luxembourg (01-01-2010)
Professional Affiliations and Activities
- Member, International Society for Computational Biology (ISCB) (2006 - present)
- Member, American Association for Cancer Research (AACR) (2010 - present)
Publications
- Identifying master regulators of cancer and their downstream targets by integrating genomic and epigenomic features. Pac Symp Biocomput. 2013: 123-34
- Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology. 2012; (2): 387-96
- Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res. 2012; (15): 3725-34
- A seven-gene set associated with chronic hypoxia of prognostic importance in hepatocellular carcinoma. Clin Cancer Res. 2010; (16): 4278-88
- Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Cancer Res. 2009; (23): 9065-72
- Recurrent copy number alterations in BRCA1-mutated ovarian tumors alter biological pathways. Hum Mutat. 2009; (12): 1693-702
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