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Nadia Tahiri is particularly interested in establishing the mathematical and statistical foundations for solving the difficult problem of phylogenetic tree classification and creating a new open-source platform for biologists to use our new methods. She will define new optimization criteria for the construction of multiple alternative consensus trees and supertrees using clustering algorithms and several important phylogenetic tree metrics. The team proposes the following three main thrusts to create a computational framework to study gene evolutionary history: (1) the development of new efficient algorithms to classify and construct a plausible set of several alternative consensus trees and supertrees characterizing the available data, (2) the exploration of evolutionary patterns of Aminoacyl-tRNA synthetases, and (3) the design and implementation of a new opensource software platform containing all the algorithms developed in our research lab as well as real and simulated data.
The long-term goal of our research lab is to develop an innovative systematic and automated approach to reconstruct different scenarios of the genetic evolution of other species groups affected by reticulate evolutionary events. For example, our research will contribute to the inference of multiple alternative subtrees of the tree of life that gathers crucial information about the biodiversity and evolutionary relationships of all living organisms.
Research Interests
Papers共 24 篇Author StatisticsCo-AuthorSimilar Experts
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Ecology and Evolutionno. 8 (2024): n/a-n/a
Wanlin Li,Nadia Tahiri
Virusesno. 7 (2024): 1133
Workshop on Algorithms in Bioinformaticspp.16:1-16:24, (2024)
Symmetryno. 7 (2024): 790
ICMHIpp.159-163, (2023)
arxiv(2023)
NEURO-ONCOLOGY ADVANCESno. 1 (2023)
Wanlin Li,Nadia Tahiri
Proceedings of the Python in Science Conferences (2023)
BIOINFORMATICSpp.212-219, (2023)
Nail Chabane, Mohamed Achraf Bouaoune, Reda Amir Sofiane Tighilt,Bogdan Mazoure,Nadia Tahiri,Vladimir Makarenkov
Studies in Classification, Data Analysis, and Knowledge Organization Classification and Data Science in the Digital Agepp.83-91, (2023)
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Author Statistics
#Papers: 23
#Citation: 62
H-Index: 4
G-Index: 7
Sociability: 3
Diversity: 1
Activity: 5
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