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

• Ana Laura Chenoweth Galaz, Nadia Tahiri. (2025). aPhyloGeo: a Python application for correlating genetic and climatic conditions. Bioinformatics. DOI
• Mahsa Farnia, Nadia Tahiri. (2024). New generalized metric based on branch length distance to compare B cell lineage trees. Algorithms for Molecular Biology. DOI
• 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
• Nadia Tahiri, Andrey Veriga, Aleksandr Koshkarov, Boris Morozov. (2022). Invariant transformers of Robinson and Foulds distance matrices for Convolutional Neural Network. Journal of Bioinformatics and Computational Biology. 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
• 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
• (2018). A new fast method for inferring multiple consensus trees using k-medoids.
• 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.
• 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).
• Justin Gagnon, Nadia Tahiri. (2024). Ecological and Spatial Influences on the Genetics of Cumacea (Crustacea: Peracarida) in the Northern North Atlantic. DOI
• 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 ou supervisés 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

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