What is he best known for?
The fields of study he is best known for:
Luay Nakhleh focuses on Phylogenetic tree, Evolutionary biology, Phylogenetics, Phylogenetic network and Coalescent theory.
His work carried out in the field of Phylogenetic tree brings together such families of science as Inference, Data mining and Botany.
His Evolutionary biology research is multidisciplinary, relying on both Tree, Theoretical computer science and Human genetics.
The Computational phylogenetics, Gene tree and Tree rearrangement research Luay Nakhleh does as part of his general Phylogenetics study is frequently linked to other disciplines of science, such as Decision tree model, therefore creating a link between diverse domains of science.
His Phylogenetic network study deals with Algorithm intersecting with Hybrid speciation and Heuristic.
Coalescent theory is a subfield of Genetics that Luay Nakhleh explores.
His most cited work include:
- PhyloNet: a software package for analyzing and reconstructing reticulate evolutionary relationships. (277 citations)
- Species tree inference by minimizing deep coalescences. (181 citations)
- Phylogenetic Networks: Modeling, Reconstructibility, and Accuracy (174 citations)
What are the main themes of his work throughout his whole career to date?
Luay Nakhleh mostly deals with Phylogenetic tree, Phylogenetics, Phylogenetic network, Coalescent theory and Evolutionary biology.
His Phylogenetic tree study integrates concerns from other disciplines, such as Algorithm and Inference.
His Phylogenetics research integrates issues from Tree, Computational biology and Population genetics.
His Phylogenetic network research is multidisciplinary, incorporating elements of Probabilistic logic, Theoretical computer science, Data mining and Horizontal gene transfer.
His research in Coalescent theory intersects with topics in Single-nucleotide polymorphism, Gene flow, Locus and Bayesian inference.
His Evolutionary biology research incorporates themes from Reticulate, Introgression, Gene duplication, Phylogenomics and Reticulate evolution.
He most often published in these fields:
- Phylogenetic tree (47.24%)
- Phylogenetics (31.90%)
- Phylogenetic network (31.90%)
What were the highlights of his more recent work (between 2017-2020)?
- Coalescent theory (28.83%)
- Inference (25.77%)
- Phylogenetic network (31.90%)
In recent papers he was focusing on the following fields of study:
Luay Nakhleh mainly focuses on Coalescent theory, Inference, Phylogenetic network, Computational biology and Phylogenetic tree.
His Coalescent theory study combines topics from a wide range of disciplines, such as Population model, Genome, Bayesian inference, Gene duplication and Algorithm.
He has researched Inference in several fields, including Linear programming, Maximum parsimony, Probabilistic logic and Biological data.
The concepts of his Phylogenetic network study are interwoven with issues in Theoretical computer science, Data mining and Introgression.
Luay Nakhleh focuses mostly in the field of Phylogenetic tree, narrowing it down to topics relating to Tree based and, in certain cases, Computational complexity theory.
Luay Nakhleh works mostly in the field of Phylogenetics, limiting it down to topics relating to Evolutionary biology and, in certain cases, Reticulate.
Between 2017 and 2020, his most popular works were:
- Inferring Phylogenetic Networks Using PhyloNet (104 citations)
- Coestimating Reticulate Phylogenies and Gene Trees from Multilocus Sequence Data. (72 citations)
- Embracing heterogeneity: coalescing the Tree of Life and the future of phylogenomics. (56 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Algorithm
- Statistics
His primary areas of investigation include Coalescent theory, Phylogenetic network, Phylogenetic tree, Computational biology and Bayesian inference.
His Coalescent theory study improves the overall literature in Phylogenetics.
Many of his studies on Phylogenetics apply to Evolutionary biology as well.
His research integrates issues of Statistical inference, Selection, Allele, Bayes' theorem and DNA sequencing in his study of Computational biology.
His Bayesian inference research integrates issues from Maximum parsimony and Markov chain Monte Carlo.
The study incorporates disciplines such as Genome, Data mining and Biological data in addition to Inference.
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