(For a full list see below or go to Google Scholar, ResearcherID, researchgate)
New generalized metric based on branch length distance to compare B cell lineage trees
Mahsa Farnia and Nadia Tahiri
Algorithms for Molecular Biology, 19(22)
Comparison of phylogenetic trees defined on different but mutually overlapping sets of taxa: A review
Wanlin Li, Aleksandr Koshkarov, and Nadia Tahiri
Ecology and Evolution, 14(8), e70054
Host-Virus Cophylogenetic Trajectories: Investigating Molecular Relationships between Coronaviruses and Bat Hosts
Wanlin Li and Nadia Tahiri (2024)
Viruses, 16(7)
Novel Algorithm for Comparing Phylogenetic Trees with Different but Overlapping Taxa
Aleksandr Koshkarov and Nadia Tahiri (2024)
Symmetry, 16(7)
GPTree Cluster: phylogenetic tree cluster generator in the context of supertree inference
Aleksandr Koshkarov and Nadia Tahiri (2023)
Bioinformatics Advances, 3(1)
Intelligent personalized shoppingrecommendation using clustering andsupervised machine learning algorithms
Nail Chabane, Achraf Bouaoune, Reda Tighilt, Moloud Abdar, Alix Boc, Étienne Lord, Nadia Tahiri, Bogdan Mazoure, Rajendra Acharya, and Vladimir Makarenkov (2022)
PloS ONE, 17(12)
Invariant transformers of Robinson and Foulds distance matrices for Convolutional Neural Network
Nadia Tahiri, Andrey Veriga, Aleksandr Koshkarov, and Boris Morozov (2022)
Journal of Bioinformatics and Computational Biology, 20(4)
Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance
Nadia Tahiri, Bernard Fichet, and Vladimiri Makarenkov (2022)
Bioinformatics, 38(13)
Quantitative structure-activity relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta
Laura Lévêque, Nadia Tahiri, Michael-Rock Goldsmith, and Marc-André Verner (2022)
Computational Toxicology, Volume 21
DoubleRecViz: a web-based tool for visualizing transcript–gene–species tree reconciliation
Esaie Kuitche, Yanchun Qi, Nadia Tahiri, Jack Parmer, and Aïda Ouangraoua (2021)
Bioinformatics, 37(13)
A deep learning approach for building multiple trees classification
Nadia Tahiri (2019)
bioRxiv
A new fast method for inferring multiple consensus trees using k-medoids
Nadia Tahiri, Matthieu Willems, and Vladimir Makarenkov (2018)
BMC Evolutionary Biology, 18(48)
Building explicit hybridization networks using the maximum likelihood and Neighbor-Joining approaches
Matthieu Willems, Nadia Tahiri, and Vladimir Makarenkov (2018)
Archives of Data Science, 4(1)
A new fast method for detecting and validating horizontal gene transfer events using phylogenetic trees and aggregation functions
Dunarel Badescu, Nadia Tahiri, and Vladimir Makarenkov (2015)
Pattern Recognition in Computational Molecular Biology: Techniques and Approaches
A new efficient algorithm for inferring explicit hybridization networks following the Neighbor-Joining principle
Matthieu Willems, Nadia Tahiri, and Vladimir Makarenkov (2014)
Journal of bioinformatics and computational biology, 12(5)
Inferring multiple consensus trees and supertrees using clustering: A review
Vladimir Makarenkov, Gayane S Barseghyan, and Nadia Tahiri (2023)
Data Analysis and Optimization: In Honor of Boris Mirkin’s 80th Birthday
New metrics for classifying phylogenetic trees using k-means and the symmetric difference metric
Nadia Tahiri and Aleksandr Koshkarov (2023)
Classification and Data Science in the Digital Age
phyDBSCAN: phylogenetic tree density-based spatial clustering of applications with noise and automatically estimated hyperparameters
Nadia Tahiri (2024)
International Federation of Classification Societies, 1(18)
A new metric to classify B cell lineage tree
Nadia Tahiri (2024)
International Federation of Classification Societies, 1(18)
Finding Maximum Common Contractions Between Phylogenetic Networks
Bertrand Marchand, Nadia Tahiri, Olivier Tremblay-Savard, and Manuel Lafond (2024)
arXiv preprint arXiv:2405.16713
A new algorithm for building comprehensive consensus tree
Md Habibur Rahman Sifat and Nadia Tahiri (2024)
AAAI Graphs and more Complex Structures For Learning and Reasoning (GCLR)
A new machine learning workflow to create an optimal waiting list in hospitals
Mouhamadou Moustapha Mbaye, Fadi MH Abu Salem, and Nadia Tahiri (2023)
Proceedings of the 2023 7th International Conference on Medical and Health Informatics
GPTree: Generator of Phylogenetic Trees with Overlapping and Biological Events for Supertree Inference
Aleksandr Koshkarov and Nadia Tahiri (2023)
16th International Joint Conference on Biomedical Engineering Systems and Technologies
aPhyloGeo-Covid: A web interface for reproducible phylogeographic analysis of SARS-CoV-2 variation using Neo4j and Snakemake
Wanlin Li and Nadia Tahiri (2023)
Proceedings of the 22nd Python in Science Conference
Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations
Nail Chabane, Mohamed Achraf Bouaoune, Reda Amir Sofiane Tighilt, Bogdan Mazoure, Nadia Tahiri, and Vladimir Makarenkov (2022)
Conference of the International Federation of Classification Societies
Phylogeography: Analysis of genetic and climatic data of SARS-CoV-2
Aleksandr Koshkarov, Wanlin Li, My-Linh Luu, and Nadia Tahiri (2022)
Proceedings of the 21st Python in Science Conference
A novel effective ensemble model for early detection of coronary artery disease
Zahia Aouabed, Moloud Abdar, Nadia Tahiri, Jaël Champagne Gareau, and Vladimir Makarenkov (2020)
Innovation in Information Systems and Technologies to Support Learning Research (EMENA-ISTL)
An intelligent shopping list based on the application of partitioning and machine learning algorithms
Naida Tahiri, Bogdan Mazoure, and Vladimir Makarenkov (2019)
SciPy-2019
A median-based consensus rule for distance exponent selection in the framework of intelligent and weighted Minkowski clustering
Renato Cordeiro de Amorim, Nadia Tahiri, Boris Mirkin, and Vladimir Makarenkov (2017)
Data Science: Innovative Developments in Data Analysis and Clustering