tahiri.nadia

Articles de revue

• Tahiri, N; Fichet, B; Makarenkov, V. (2022). Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance. Bioinformatics (Published).
• Tahiri, N; Veriga, A; Koshkarov, A*; Morozov, B. (2022). Invariant transformers of Robinson-Foulds distance matrices for convolutional neural network. Journal of Bioinformatics and Computational Biology (Published).
• Tahiri, N; Koshkarov, A*. (2022). New metrics for classifying phylogenetic trees using k-means and the symmetric difference metric. Classification and Data Science in the Digital Age, Springer Verlag (Accepted).
• Marchitti, SA; Verner, MA; Tahiri, N; Dillingham, C; Chang, D; LaKind, JC; Hines, E; Fenton, S; Kenneke, JF; Goldsmith, MR. (2022). Predicting Breast Milk:Serum Partitioning Using QSAR Models. Chemical Research in Toxicology (Submitted).
• Lévêque, L*; Tahiri, N; Goldsmith, MR; Verner, MA. (2022). Quantitative Structure-Activity Relationship (QSAR) Modeling to Predict the Transfer of Environmental Chemicals across the Placenta. Computational Toxicology (the first two authors are equal contributors and listed in alphabetical order) (Published).
• Chabane, N; Bouaoune, MA; Tighilt, RAS; Boc, A; Lord, E; Tahiri, N; Mazoure, B; Makarenkov, V. (2022). Using Clustering and Machine Learning Algorithms for Intelligent Personalized Shopping Recommendation. IEEE Access (Submitted).
• Chabane, N; Bouaoune, MA; Tighilt, RAS; Mazoure, B; Tahiri, N; Makarenkov, V. (2022). Using clustering and machine learning methods to provide intelligent grocery shopping recommendations. Classification and Data Science in the Digital Age, Springer Verlag (Accepted).
• Kuitche, E; Qi, Y; Tahiri, N; Parmer, J; Ouangraoua, A. (2020). DoubleRecViz: A Web-Based Tool for Visualizing Transcript-Gene-Species reconciliation. Bioinformatics 37 (13), 1920-1922. (Published).
• Tahiri, N; Willems, M; Makarenkov, V. (2018). A new fast method for inferring multiple consensus trees using k-medoids. BMC evolutionary biology 18 (48), 1-12. DOI. (Published).
• Willems, M; Tahiri, N; Makarenkov, V. (2018). Building explicit hybridization networks using the maximum likelihood and Neighbor-Joining approaches. Archives of Data Science, Series A 4 (1), 1-17. DOI. (Published).
• Willems, M; Tahiri, N*; Makarenkov, V. (2015). A new efficient algorithm for inferring explicit hybridization networks following the Neighbor-Joining principle. Journal of Bioinformatics and Computational Biology 12 (5), 1450024. DOI. (Published).

Chapitres de livre

• Makarenkov, V; Barseghyan, GS*; Tahiri, N. (2022). Inferring multiple consensus trees and supertrees using clustering: a review. Data Mining is More Than Comprehensive Analysis (1-33). Springer Nature. (Submitted).
• Cordeiro de Amorim, R; 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. Data Science (97-110). Springer Verlag. DOI. (Published).
• Badescu, D; Tahiri, N; Makarenkov, V. (2016). 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. (1, 483-504). Wiley. DOI. (Published).

Articles de conférence

• Tahiri, N; Koshkarov, A*. (2022). New metrics for classifying phylogenetic trees using k-means and the symmetric difference metric. International Federation of Classification Societies (IFCS). (Accepted).
• Li, W*; Koshkarov, A*; Luu, ML*; Tahiri, N. (2022). Phylogeography: Analysis of genetic and climatic data of SARS-CoV-2. Scientific Computing with Python (SciPy). (Submitted).
• Koshkarov, A*; Li, W*; Luu, ML*; Tahiri, N. (2022). Phylogeography: Analysis of genetic and climatic data of SARS-CoV-2. Scientific Computing with Python (SciPy). (Accepted).
• Chabane, N; Bouaoune, MA; Tighilt, RAS; Mazoure, B; Tahiri, N; Makarenkov, V. (2022). Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations. International Federation of Classification Societies (IFCS). (Accepted).
• Bocéno, A; Bloch, S; Tahiri, N; Verner, MA. (2021). Comparing an Acceptable Exposure Level Based on In Vitro Studies of PFOA Hepatotoxicity to Levels Measured in Epidemiologic Studies. Society of Toxicology (SOT), virtual conference. (Published).
• Tahiri, N. (2021). Invasive insects through phylogeography. 12th Annual symposium of Quebec Centre for biodiversity science (QCBS). (Published).
• Li, W*; Luu, ML*; Tahiri, N. (2021). La phylogéographie : à la recherche de la vérité lorsque tout est en mouvement. Nuit des chercheuses et des chercheurs (Finaliste du concours de vulgarisation scientifique). (Accepted).
• Lévêque, L*; Tahiri, N; Goldsmith, MR; Verner, MA. (2021). Quantitative Structure-Activity Relationship (QSAR) Modeling to Predict the Transfer of Environmental Chemicals Across the Placenta. American Society for Cellular and Computational Toxicology (ASCCT). (Published).
• 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 (Springer), 480-489. (Published).
• Bocéno, A; Bloch, S; Tahiri, N; Verner, MA. (2020). A case study evaluating the use of in vitro data on perfluorooctanoic acid (PFOA) hepatotoxicity to derive acceptable exposure levels. The Society of Toxicology of Canada (STC), virtual conference. (Published).
• Tahiri, N; Lévêque, L*; Verner, MA. (2020). Predicting the Transfer of Chemicals through Lactation Using Quantitative Structure-Activity Relationship (QSAR) Modeling. Society of Toxicology (SOT). (Published).
• Tahiri, N; Mazoure, B; Makarenkov, V. (2019). An intelligent shopping list based on the application of partitioning and machine learning algorithms. Proceedings of the 18th Python in Science Conference, 85 - 92. (Published).
• Lévêque, L*; Tahiri, N; Verner, MA. (2019). Predicting the placental transfer of chemicals using quantitative structure-activity relationship (QSAR) modeling. Society of Toxicology of Canada (STC). (Published).
• Tahiri, N; Lévêque, L*; Verner, MA. (2019). Quantitative structure-activity relationship (QSAR) modeling as a tool to assess lactational exposure for data-poor chemicals. Society of toxicology of Canada (STC). (Published).
• Tahiri, N. (2018). A new fast method for inferring multiple consensus trees using k-medoids. Canadian Celebration of Women in Computing (CAN-CWiC). (Published).
• Tahiri, N. (2018). An intelligent shopping list based on partitioning and machine learning algorithms. Neural Information Processing Systems (NeurIPS). (Published).
• Tahiri, N. (2017). A new clustering method for building multiple supertrees using k-means. Proceedings of NIPS-2017, (Published).
• Tahiri, N; Willems, M; Makarenkov, V. (2017). A new fast method for building multiple consensus trees using k-medoids. Proceedings of SMC-2017, 31-37. (Published).
• Tahiri, N; Badran, N; Dion-Phénix, H; Meniaï, I; Makarenkov, V. (2017). Avancement des connaissances en bioinformatique en développant un nouvel algorithme pour l’analyse des arbres phylogeographiques. Association Canadienne-Française pour l'Avancement des Sciences (ACFAS). (Published).
• Tahiri, N; Badran, N. (2016). New algorithm to find the relation between genetic and geographic distribution of species. Symposium Sciences biologiques. (Published).
• Tahiri, N*; Willems, M; Makarenkov, V. (2015). Inférence des super-arbres multiples en utilisant l'algorithme des k-moyennes. Proceedings of SFC-2015, 110-114. (Published).
• Cordeiro de Amorim, R; Tahiri, N*; Mirkin, BG; Makarenkov, V. (2015). Minkowski weighted k-means clustering with amedian-based consensus rule. Proceedings of IFCS-2015, 90-110. DOI. (Published).
• Tahiri, N; Willems, M; Makarenkov, V. (2014). Classification d’arbres phylogénétiques basée surl’algorithme des k-moyennes. Proceedings of SFC-2014, 49-54. (Published).
• Tahiri, N*; Boc, A; Willems, M*; Makarenkov, V. (2012). Classification des langues Indo-Européennes basée sur un modèle d’identification de transferts horizontaux de gènes. Proceedings of SFC-2012, (Published).