F
Fantine Mordelet
Researcher at Duke University
Publications - 8
Citations - 2750
Fantine Mordelet is an academic researcher from Duke University. The author has contributed to research in topics: Inference & Support vector machine. The author has an hindex of 7, co-authored 8 publications receiving 2408 citations. Previous affiliations of Fantine Mordelet include Mines ParisTech.
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Wisdom of crowds for robust gene network inference
Daniel Marbach,James C. Costello,Robert Küffner,Nicole M. Vega,Robert J. Prill,Diogo M. Camacho,Kyle R. Allison,Andrej Aderhold,Richard Bonneau,Yukun Chen,James J. Collins,Francesca Cordero,Martin Crane,Frank Dondelinger,Mathias Drton,Roberto Esposito,Rina Foygel,Alberto de la Fuente,Jan Gertheiss,Pierre Geurts,Alex Greenfield,Marco Grzegorczyk,Anne-Claire Haury,Benjamin Holmes,Torsten Hothorn,Dirk Husmeier,Vân Anh Huynh-Thu,Alexandre Irrthum,Manolis Kellis,Guy Karlebach,Sophie Lèbre,Vincenzo De Leo,Aviv Madar,Subramani Mani,Fantine Mordelet,Harry Ostrer,Zhengyu Ouyang,Ravi Pandya,Tobias Petri,Andrea Pinna,Christopher S. Poultney,Serena Rezny,Heather J. Ruskin,Yvan Saeys,Ron Shamir,Alina Sîrbu,Mingzhou Song,Nicola Soranzo,Alexander Statnikov,Gustavo Stolovitzky,Nicci Vega,Paola Vera-Licona,Jean-Philippe Vert,Alessia Visconti,Haizhou Wang,Louis Wehenkel,Lukas Windhager,Yang Zhang,Ralf Zimmer +58 more
TL;DR: A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.
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TIGRESS: Trustful Inference of Gene REgulation using Stability Selection
Anne-Claire Haury,Fantine Mordelet,Paola Vera-Licona,Paola Vera-Licona,Paola Vera-Licona,Jean-Philippe Vert,Jean-Philippe Vert,Jean-Philippe Vert +7 more
TL;DR: A novel, robust and accurate scoring technique for stability selection, which improves the performance of feature selection with LARS, is introduced, which was ranked among the top GRN inference methods in the DREAM5 gene network inference challenge and was evaluated to be the best linear regression-based method in the challenge.
Posted Content
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection
Anne-Claire Haury,Fantine Mordelet,Paola Vera-Licona,Paola Vera-Licona,Paola Vera-Licona,Jean-Philippe Vert,Jean-Philippe Vert,Jean-Philippe Vert +7 more
TL;DR: TIGRESS (Trustful Inference of Gene Regression using Stability Selection) as discussed by the authors is the state-of-the-art method for gene regulatory network inference using least angle regression (LARS) and stability selection.
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A bagging SVM to learn from positive and unlabeled examples
TL;DR: It is shown theoretically and experimentally that the proposed method can match and even outperform the performance of state-of-the-art methods for PU learning, particularly when the number of positive examples is limited and the fraction of negatives among the unlabeled examples is small.
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SIRENE: supervised inference of regulatory networks.
TL;DR: SIRENE (Supervised Inference of Regulatory Networks), a new method for the inference of gene regulatory networks from a compendium of expression data, is proposed and test it on a benchmark experiment aimed at predicting regulations in Escherichia coli, and it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.