O
Olivier Gevaert
Researcher at Stanford University
Publications - 212
Citations - 9764
Olivier Gevaert is an academic researcher from Stanford University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 40, co-authored 163 publications receiving 6911 citations. Previous affiliations of Olivier Gevaert include Broad Institute & Ford Motor Company.
Papers
More filters
Journal ArticleDOI
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
Tathiane M. Malta,Artem Sokolov,Andrew J. Gentles,Tomasz Burzykowski,Laila M. Poisson,John N. Weinstein,Bozena Kaminska,Joerg Huelsken,Larsson Omberg,Olivier Gevaert,Antonio Colaprico,Patrycja Czerwińska,Sylwia Mazurek,Lopa Mishra,Holger Heyn,Alexander Krasnitz,Andrew K. Godwin,Alexander J. Lazar,Joshua M. Stuart,Katherine A Hoadley,Peter W. Laird,Houtan Noushmehr,Maciej Wiznerowicz +22 more
TL;DR: Novel stemness indices for assessing the degree of oncogenic dedifferentiation are provided and it is found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors.
Journal ArticleDOI
Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology
Lonneke A.M. Gravendeel,Mathilde C.M. Kouwenhoven,Olivier Gevaert,Johan J. de Rooi,Andrew P. Stubbs,J Elza Duijm,Anneleen Daemen,Fonnet E. Bleeker,Linda B. C. Bralten,Nanne K. Kloosterhof,Bart De Moor,Paul H. C. Eilers,Peter J. van der Spek,Johan M. Kros,Peter A. E. Sillevis Smitt,Martin J. van den Bent,Pim J. French +16 more
TL;DR: The data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification, and molecular classification therefore may aid diagnosis and can guide clinical decision making.
Journal ArticleDOI
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
Shuo Wang,Mu Zhou,Zaiyi Liu,Zhenyu Liu,Dongsheng Gu,Yali Zang,Di Dong,Olivier Gevaert,Jie Tian +8 more
TL;DR: The proposed data‐driven model, termed the Central Focused Convolutional Neural Networks (CF‐CNN), to segment lung nodules from heterogeneous CT images achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively.
Journal ArticleDOI
Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results
Olivier Gevaert,Jiajing Xu,Chuong D. Hoang,Ann N. Leung,Yue Xu,Andrew Quon,Daniel L. Rubin,Sandy Napel,Sylvia K. Plevritis +8 more
TL;DR: This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.
Journal ArticleDOI
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
TL;DR: This work evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group.