Vân Anh Huynh-Thu
See also Google Scholar.
2023
Knowledge-guided additive modeling for supervised regression
Claes Y., Huynh-Thu V. A. and Geurts P.
https://link.springer.com/chapter/10.1007/978-3-031-45275-8_5
https://arxiv.org/abs/2307.02229
Optimizing model-agnostic Random Subspace ensembles
Huynh-Thu V. A. and Geurts P.
https://link.springer.com/article/10.1007/s10994-023-06427-5
https://arxiv.org/abs/2109.03099
Distinct blood protein profiles associated with the risk of short-term and mid/long-term clinical relapse in patients with Crohn’s disease stopping infliximab: when the remission state hides different types of residual disease activity
Pierre N., Huynh-Thu V. A., Marichal T., Allez M., Bouhnik Y., Laharie D., Boureille A., Colombel J.-F., Meuwis M.-A., Louis E., and GETAID
https://gut.bmj.com/content/early/2022/08/25/gutjnl-2022-327321.long
From global to local MDI variable importances for random forests and when they are Shapley values
Sutera A., Louppe G., Huynh-Thu V. A., Wehenkel L., and Geurts P.
https://proceedings.neurips.cc/paper/2021/hash/1cfa81af29c6f2d8cacb44921722e753-Abstract.html
https://arxiv.org/abs/2111.02218
Discovery of biomarker candidates associated with the risk of short-term and mid/long-term relapse after infliximab withdrawal in Crohn’s patients: a proteomics-based study
Pierre N., Baiwir D.*, Huynh-Thu V. A.*, Mazzucchelli G., Smargiasso N., De Pauw E., Bouhnik Y., Laharie D., Colombel J.-F., Meuwis M.-A.#, Louis E.#, and GETAID
*, #: Contributed equally.
https://gut.bmj.com/content/early/2020/10/25/gutjnl-2020-322100.full
https://orbi.uliege.be/handle/2268/252076
Nets versus trees for feature ranking and gene network inference
Vecoven N., Begon J.-M., Sutera A., Geurts P., and Huynh-Thu V. A.
https://link.springer.com/chapter/10.1007%2F978-3-030-61527-7_16
https://orbi.uliege.be/handle/2268/252077
Gene Regulatory Networks
Sanguinetti G. and Huynh-Thu V. A. (editors)
https://link.springer.com/book/10.1007/978-1-4939-8882-2
Gene regulatory network inference: An Introductory Survey
Huynh-Thu V. A. and Sanguinetti G.
https://link.springer.com/protocol/10.1007/978-1-4939-8882-2_1
https://arxiv.org/abs/1801.04087
Unsupervised Gene Network Inference with Decision Trees and Random Forests
Huynh-Thu V. A. and Geurts P.
https://link.springer.com/protocol/10.1007/978-1-4939-8882-2_8
https://orbi.uliege.be/handle/2268/230326
Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3
Huynh-Thu V. A. and Sanguinetti G.
https://link.springer.com/protocol/10.1007/978-1-4939-8882-2_9"
https://orbi.uliege.be/handle/2268/230320
dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data
Huynh-Thu V. A. and Geurts P.
https://www.nature.com/articles/s41598-018-21715-0
SCENIC: single-cell regulatory network inference and clustering
Aibar S., González-Blas C. B., Moerman T., Huynh-Thu V. A., Imrichova H., Hulselmans G., Rambow F., Marine J.-C., Geurts P., Aerts J., van den Oord J., Atak Z. K., Wouters J., and Aerts S.
https://www.nature.com/articles/nmeth.4463?WT.feed_name=subjects_biotechnology
http://orbi.uliege.be/handle/2268/216155
Context-dependent feature analysis with random forests
Sutera A., Louppe G., Huynh-Thu V. A., Wehenkel L., and Geurts P.
http://www.auai.org/uai2016/proceedings/papers/253.pdf
http://www.auai.org/uai2016/proceedings/supp/253_supp.pdf
Strand-specific, high-resolution mapping of modified RNA polymerase II
Milligan L., Huynh-Thu V. A., Delan-Forino C., Tuck A., Petfalski E., Lombraña R., Sanguinetti G., Kudla G., and Tollervey D.
http://onlinelibrary.wiley.com/doi/10.15252/msb.20166869/abstract
Combining tree-based and dynamical systems for the inference of gene regulatory networks
Huynh-Thu V. A. and Sanguinetti G.
http://bioinformatics.oxfordjournals.org/content/31/10/1614
Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference
Potier D., Davie K., Hulselmans G., Naval Sanchez M., Haagen L., Huynh-Thu V. A., Koldere D., Celik A., Geurts P., Christiaens V., and Aerts S.
http://www.cell.com/cell-reports/abstract/S2211-1247(14)01004-3
Bridging physiological and evolutionary time-scales in a gene regulatory network
Marchand G., Huynh-Thu V. A., Kane N., Arribat S., Varès D., Rengel D., Balzergue S., Rieseberg L., Vincourt P., Geurts P., Vignes M., and Langlade N. B.
http://onlinelibrary.wiley.com/doi/10.1111/nph.12818/abstract
NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance algorithms
Ruyssinck J., Huynh-Thu V. A., Geurts P., Dhaene T., Demeester P., and Saeys Y.
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0092709
Identification of a microRNA landscape targeting the PI3K/Akt signaling pathway in inflammation-induced colorectal carcinogenesis
Josse C., Bouznad N., Geurts P., Irrthum A., Huynh-Thu V. A., Servais L., Hego A., Delvenne P., Bours V., and Oury C.
http://ajpgi.physiology.org/content/306/3/G229
Gene regulatory network inference from systems genetics data using tree-based methods
Huynh-Thu V. A., Wehenkel L., and Geurts P.
http://link.springer.com/chapter/10.1007%2F978-3-642-45161-4_5
http://orbi.uliege.be/handle/2268/156498
Myelin-derived lipids modulate macrophage activity by liver X receptor activation
Bogie J. F. J., Timmermans S., Huynh-Thu V. A., Irrthum A., Smeets H. J. M., Gustafsson J.-A., Steffensen K. R., Mulder M., Stinissen P., Hellings N., Hendriks J. J. A.
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0044998
Wisdom of crowds for robust gene network inference
Marbach D., Costello J. C., Küffner R., Vega N., Prill R. J., Camacho D. M., Allison K. R., the DREAM5 consortium (including Geurts P., Huynh-Thu V. A., Irrthum A., Saeys Y., and Wehenkel L.), Kellis M., Collins J. J., and Stolovitzky G.
https://www.nature.com/articles/nmeth.2016
http://orbi.uliege.be/handle/2268/127819
Statistical interpretation of machine learning-based feature importance scores for biomarker discovery
Huynh-Thu V. A., Saeys Y., Wehenkel L., and Geurts P.
http://bioinformatics.oxfordjournals.org/content/28/13/1766.short
Machine learning-based feature ranking: Statistical interpretation and gene network inference
Huynh-Thu V. A., PhD thesis
http://hdl.handle.net/2268/108611
Slides of the defense here.
MicroRNAs profiling in murine models of acute and chronic asthma: a relationship with mRNAs targets
Garbacki N., Di Valentin E., Huynh-Thu V. A., Geurts P., Irrthum A., Crahay C., Arnould T., Deroanne C., Piette J., Cataldo D., and Colige A.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0016509
Inferring regulatory networks from expression data using tree-based methods
Huynh-Thu V. A., Irrthum A., Wehenkel L., and Geurts P.
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012776
Exploiting tree-based variable importances to selectively identify relevant variables
Huynh-Thu V. A., Wehenkel L., and Geurts P.
http://jmlr.csail.mit.edu/proceedings/papers/v4/huynhthu08a/huynhthu08a.pdf