What is he best known for?
The fields of study he is best known for:
Tree, Phylogenetic tree, Algorithm, Genetics and Phylogenetics are his primary areas of study.
His work deals with themes such as Software, Unix, Phylogenomics, Workstation and Partition, which intersect with Tree.
His work carried out in the field of Phylogenomics brings together such families of science as Maximum likelihood, Inference and Heuristics.
The study incorporates disciplines such as Data mining, Bioinformatics, Model selection, Tree and Haplotype in addition to Phylogenetic tree.
In his study, CPU time, Bootstrap aggregating, Resampling and Likelihood-ratio test is strongly linked to Range, which falls under the umbrella field of Algorithm.
His studies deal with areas such as Evolutionary biology, Sequence and Artificial intelligence as well as Phylogenetics.
His most cited work include:
- IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies (6114 citations)
- ModelFinder: fast model selection for accurate phylogenetic estimates (2638 citations)
- ModelFinder: fast model selection for accurate phylogenetic estimates (2638 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Phylogenetic tree, Phylogenetics, Genetics, Algorithm and Tree.
Arndt von Haeseler has researched Phylogenetic tree in several fields, including Evolutionary biology, Sequence, Software and Theoretical computer science.
Particularly relevant to Phylogenomics is his body of work in Phylogenetics.
Arndt von Haeseler works mostly in the field of Genetics, limiting it down to topics relating to Computational biology and, in certain cases, Peptide sequence, as a part of the same area of interest.
His Algorithm study combines topics in areas such as Substitution model, Multiple sequence alignment, Sequence alignment, Inference and Range.
His Tree study which covers Maximum likelihood that intersects with Phylogenetic inference and Artificial intelligence.
He most often published in these fields:
- Phylogenetic tree (39.64%)
- Phylogenetics (24.29%)
- Genetics (23.57%)
What were the highlights of his more recent work (between 2018-2021)?
- Computational biology (20.71%)
- Cell biology (15.71%)
- Tree (20.00%)
In recent papers he was focusing on the following fields of study:
Arndt von Haeseler focuses on Computational biology, Cell biology, Tree, Phylogenetic tree and Sequence.
His Computational biology research is multidisciplinary, incorporating elements of Genome, Gene, Sex Maturation, Single-nucleotide polymorphism and Proteome profiling.
His study explores the link between Tree and topics such as Phylogenetic inference that cross with problems in Machine learning.
His studies in Machine learning integrate themes in fields like Phylogenomics and Software.
The concepts of his Phylogenetic tree study are interwoven with issues in Artificial neural network, Algorithm and Phylogenetics.
His Sequence research focuses on Combinatorics and how it relates to State.
Between 2018 and 2021, his most popular works were:
- IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. (543 citations)
- IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. (543 citations)
- GHOST: Recovering Historical Signal from Heterotachously Evolved Sequence Alignments. (30 citations)
In his most recent research, the most cited papers focused on:
His primary areas of investigation include Phylogenetics, Computational biology, Phylogenetic tree, Evolutionary biology and Phylogenetic inference.
His Phylogenetics study combines topics from a wide range of disciplines, such as Mixture model and Inference.
His research integrates issues of Nucleic acid, Pipeline, Nucleotide, DNA microarray and DNA sequencing in his study of Computational biology.
His research in Phylogenetic tree intersects with topics in Mutation, Mutation rate and Allele frequency.
The various areas that Arndt von Haeseler examines in his Phylogenetic inference study include Sequence, Tree, Maximum likelihood, Artificial intelligence and Software.
His Tree research incorporates themes from Machine learning, Phylogenomics and Key.
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