Nadia Tahiri

Financement

• Subvention. New algorithms for inferring multiple alternative consensus trees and supertrees. Natural Sciences and Engineering Research Council (Ottawa, Canada). 25 000 $. (2023-2024).
  Numéro de subvention : RGPIN-2022-04322. Voir plus
• Subvention. Nouvelle approche du regroupement des arbres phylogénétiques par l'exploration du projet de l'Arbre de Vie. Fonds de Recherche du Québec – Nature et Technologies (Montreal, Canada). 78 000 $. (2023-2025).
  Numéro de subvention : 326911. Voir plus
• Subvention. Novel Metrics for the Comparison of Phylogenetic Networks. Natural Sciences and Engineering Research Council (Ottawa, Canada). 45 000 $. (2023-2024).
  Numéro de subvention : 577123-2022. Voir plus
• Subvention. Nouvelles métriques pour la comparaison de réseaux phylogénétiques. Fonds de recherche du Québec (Québec, Canada). 114 300 $. (2022-2026).
• Subvention. New algorithms for inferring multiple alternative consensus trees and supertrees. Natural Sciences and Engineering Research Council (Ottawa, Canada). 12 500 $. (2022-2023).
  Numéro de subvention : DGECR-2022-00395. Voir plus
• Subvention. Élaboration d'une nouvelle approche pour évaluer les risques liés à l'exposition des enfants aux contaminants de l'environnement. Fonds de Recherche du Québec - Santé (Montreal, Canada). 90 000 $. (2020-2022).
  Numéro de subvention : 288549. Voir plus
• Subvention. Algorithmes bioinformatiques pour la reconstruction d'arbres consensus et de superarbresmultiples. Fonds de Recherche du Québec – Nature et Technologies (Montreal, Canada). 46 666 $. (2013-2016).
  Numéro de subvention : 173808. Voir plus
• Subvention. Applied machine learning for health insurance fraud detection. Mitacs (Vancouver, Canada).
  Numéro de subvention : grant.13965807. Voir plus

Publications

Articles

• Wanlin Li, Nadia Tahiri. (2024). Host–Virus Cophylogenetic Trajectories: Investigating Molecular Relationships between Coronaviruses and Bat Hosts. Viruses. DOI
• Aleksandr Koshkarov, Nadia Tahiri. (2024). Novel Algorithm for Comparing Phylogenetic Trees with Different but Overlapping Taxa. Symmetry. DOI
• Li, W., Koshkarov, A., Tahiri, N. (2024). Comparison of phylogenetic trees defined on different but mutually overlapping sets of taxa: A review. Ecology and Evolution. DOI
• Koshkarov, A., Tahiri, N. (2023). GPTree Cluster: phylogenetic tree cluster generator in the context of supertree inference. Bioinformatics Advances. DOI
• Tahiri, N., Fichet, B., Makarenkov, V. (2022). Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance. Bioinformatics. DOI
• Chabane, N., Bouaoune, A., Tighilt, R., Abdar, M., Boc, A., Lord, E., Tahiri, N., Mazoure, B., Rajendra Acharya, U., Makarenkov, V. (2022). Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms. PLoS ONE. DOI
• Tahiri, N., Veriga, A., Koshkarov, A., Morozov, B. (2022). Invariant transformers of Robinson and Foulds distance matrices for Convolutional Neural Network. Journal of Bioinformatics and Computational Biology. DOI
• Lévêque, L., Tahiri, N., Goldsmith, M.-R., Verner, M.-A. (2022). Quantitative Structure-Activity Relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta. Computational Toxicology. DOI
• Kuitche, E., Qi, Y., Tahiri, N., Parmer, J., Ouangraoua, A. (2021). DoubleRecViz: A web-based tool for visualizing transcript-gene-species tree reconciliation. Bioinformatics. DOI
• Tahiri, N., Willems, M., Makarenkov, V. (2018). A new fast method for inferring multiple consensus trees using k-medoids. BMC Evolutionary Biology. DOI
• (2018). A new fast method for inferring multiple consensus trees using k-medoids.
• (2018). A new fast method for inferring multiple consensus trees using k-medoids.
• (2018). A new fast method for inferring multiple consensus trees using k-medoids.
• (2018). A new fast method for inferring multiple consensus trees using k-medoids.
• Willems, M., Tahiri, N., Makarenkov, V. (2014). A new efficient algorithm for inferring explicit hybridization networks following the Neighbor-Joining principle. Journal of Bioinformatics and Computational Biology. DOI

Chapitres de livre

• Makarenkov, V., Barseghyan, G.S., Tahiri, N. (2023). Inferring Multiple Consensus Trees and Supertrees Using Clustering: A Review. Springer Optimization and Its Applications. DOI
• Badescu, D., Tahiri, N., Makarenkov, V. (2015). A New Fast Method for Detecting and Validating Horizontal Gene Transfer Events Using Phylogenetic Trees and Aggregation Functions. Pattern Recognition in Computational Molecular Biology: Techniques and Approaches. DOI

Articles de conférence

• Elio Torquet, Jesper Jansson, Nadia Tahiri. (2025). Graph-based method for constructing consensus trees. Conference: 15th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2025).
• Mouhamadou Moustapha Mbaye, Fadi M H Abu Salem, Nadia Tahiri. (2023). A new machine learning workflow to create an optimal waiting list in hospitals. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI). DOI
• Tahiri, N., Koshkarov, A. (2023). New Metrics for Classifying Phylogenetic Trees Using K-means and the Symmetric Difference Metric. Studies in Classification, Data Analysis, and Knowledge Organization. DOI
• Chabane, N., Bouaoune, M.A., Tighilt, R.A.S., Mazoure, B., Tahiri, N., Makarenkov, V. (2023). Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations. Studies in Classification, Data Analysis, and Knowledge Organization. DOI
• Aouabed, Z., Abdar, M., Tahiri, N., Champagne Gareau, J., Makarenkov, V. (2020). A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease. Learning and Analytics in Intelligent Systems. DOI
• de Amorim, R.C., Tahiri, N., Mirkin, B., Makarenkov, V. (2017). A median-based consensus rule for distance exponent selection in the framework of intelligent and weighted minkowski clustering. Studies in Classification, Data Analysis, and Knowledge Organization. DOI

Autres contributions

Cours enseignés à l'UdeS

• IFT436 - Algorithmes et structures de données. (2024-2026). (3CR).
• IFT187 - Éléments de bases de données. (2024-2025). (3CR).
• BIN702 - Algorithmes pour la bio-informatique. (2024). (3CR).
• BIN710 - Forage de données pour la bio-informatique. (2024). (3CR).
• IFT287 - Exploitation de BD relationnelles et OO. (2024). (3CR).
• IFT769 - Sujets choisis en informatique théorique. (2024). (3CR).
• IFT870 - Forage de données. (2024). (3CR).
• BIN704 - Sujets choisis en bio-informatique. (2023). (3CR).

Divers

• Tahiri, N. (2019). A deep learning approach for building multiple trees classification. bioRxiv.
• Li, W., Tahiri, N. (2023). Host-Virus Cophylogeny Trajectories: Investigating Molecular Relationships between Coronaviruses and Bat Hosts. Research Square.
• Tahiri, N., Fichet, B., Makarenkov, V. (2021). Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance. bioRxiv.
• Tahiri, N., Fichet, B., Makarenkov, V. (2021). Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance. arXiv.
• Marchand, B., Tahiri, N., Tremblay-Savard, O., Lafond, M. (2024). Finding Maximum Common Contractions Between Phylogenetic Networks. arXiv.
• Makarenkov, V., Barseghyan, G.S., Tahiri, N. (2023). Inferring multiple consensus trees and supertrees using clustering: a review. arXiv.