Výsledky vyhledávání - "Fantine Mordelet"

Akademický článek

Bayesian nonparametric discovery of isoforms and individual specific quantification

Autor: Derek Aguiar, Li-Fang Cheng, Bianca Dumitrascu, Fantine Mordelet, Athma A. Pai, Barbara E. Engelhardt

Publikováno v: Nature Communications, Vol 9, Iss 1, Pp 1-12 (2018)

Alternative splicing leads to transcript isoform diversity. Here, Aguiar et al. develop biisq, a Bayesian nonparametric approach to discover and quantify isoforms from RNA-seq data.

Externí odkaz: https://doaj.org/article/6a28ab9e7cc640d796b018e6bdb3af53

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Wisdom of crowds for robust gene network inference

Autor: Daniel, Marbach, Costello, James C., Robert, Küffner, Vega, Nicole M., Prill, Robert J., Camacho, Diogo M., Allison, Kyle R., Andrej, Aderhold, Richard, Bonneau, Yukun, Chen, Collins, James J., 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, Poultney, Christopher S., Serena, Rezny, Ruskin, Heather J., Yvan, Saeys, Ron, Shamir, Sirbu, Alina, 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

Publikováno v: Nature methods

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network infer

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01bd67cd43235cc06f92f7fb3635f0f5
https://doi.org/10.1038/nmeth.2016

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Bayesian nonparametric discovery of isoforms and individual specific quantification

Autor: Fantine Mordelet, Li-Fang Cheng, Athma A. Pai, Derek Aguiar, Barbara E. Engelhardt, Bianca Dumitrascu

Publikováno v: Nature Communications
Nature Communications, Vol 9, Iss 1, Pp 1-12 (2018)

Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e21a307b1d3498d6cfd271834105f26
https://pubmed.ncbi.nlm.nih.gov/29703885

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A bagging SVM to learn from positive and unlabeled examples

Autor: Fantine Mordelet, Jean-Philippe Vert

Publikováno v: Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2014, 37, pp.201-209. ⟨10.1016/j.patrec.2013.06.010⟩

We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting. This problem, often referred to as PU learning, differs from the standard supervise

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8294ecffc6ab6bda3b45dd4bf99999d5
https://doi.org/10.1016/j.patrec.2013.06.010

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Stability selection for regression-based models of transcription factor–DNA binding specificity

Autor: John R. Horton, Barbara E. Engelhardt, Raluca Gordân, Alexander J. Hartemink, Fantine Mordelet

Publikováno v: Bioinformatics

Motivation: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::590fd189ee09e6be8a7a6cafc7b2c299
http://europepmc.org/articles/PMC3694650

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Supervised inference of gene regulatory networks from positive and unlabeled examples

Autor: Fantine, Mordelet, Jean-Philippe, Vert

Publikováno v: Methods in molecular biology (Clifton, N.J.). 939

Elucidating the structure of gene regulatory networks (GRN), i.e., identifying which genes are under control of which transcription factors, is an important challenge to gain insight on a cell's working mechanisms. We present SIRENE, a method to esti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::e545f7fcde4b2c90fb463a76a44c364c
https://pubmed.ncbi.nlm.nih.gov/23192540

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TIGRESS: Trustful Inference of Gene REgulation using Stability Selection

Autor: Anne-Claire Haury, Fantine Mordelet, Jean-Philippe Vert, Paola Vera-Licona

Publikováno v: BMC Systems Biology
BMC Systems Biology, BioMed Central, 2012, 6 (1), pp.145. ⟨10.1186/1752-0509-6-145⟩
BMC Systems Biology, BioMed Central, 2012, 6 (1), pp.145. <10.1186/1752-0509-6-145>

Background Inferring the structure of gene regulatory networks (GRN) from a collection of gene expression data has many potential applications, from the elucidation of complex biological processes to the identification of potential drug targets. It i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b3fb4fde70666aaa7e8eb31954495b6
https://www.hal.inserm.fr/inserm-00797206/document

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Supervised Inference of Gene Regulatory Networks from Positive and Unlabeled Examples

Autor: Fantine Mordelet, Jean-Philippe Vert

Publikováno v: Methods in Molecular Biology ISBN: 9781627031066

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ea0ee92efdf95032980cf3de8f34b016
https://doi.org/10.1007/978-1-62703-107-3_5

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ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

Autor: Jean-Philippe Vert, Fantine Mordelet

Publikováno v: BMC Bioinformatics
BMC Bioinformatics, Vol 12, Iss 1, p 389 (2011)
BMC Bioinformatics, BioMed Central, 2011, 12 (1), pp.389. ⟨10.1186/1471-2105-12-389⟩

Background Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene cand

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a39446c3f9055513cafe3cc1dafdaf02
http://europepmc.org/articles/PMC3215680

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Akademický článek

SIRENE: supervised inference of regulatory networks.

Autor: Fantine Mordelet, Jean-Philippe Vert

Publikováno v: Bioinformatics; Aug2008, Vol. 24 Issue 16, pi76-i76, 1p

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