What is he best known for?
The fields of study he is best known for:
- Gene
- Operating system
- Computer network
Alexander Zelikovsky mostly deals with Steiner tree problem, Approximation algorithm, Combinatorics, Discrete mathematics and Algorithm.
His research in Steiner tree problem focuses on subjects like Graph, which are connected to Theory of computation and Kruskal's algorithm.
The concepts of his Approximation algorithm study are interwoven with issues in Time complexity and Heuristics.
His work in the fields of Discrete mathematics, such as Minimum spanning tree, intersects with other areas such as k-minimum spanning tree.
His k-minimum spanning tree research is multidisciplinary, incorporating perspectives in Vantage-point tree and Metric tree.
The study incorporates disciplines such as Pursuer and Mathematical optimization in addition to Algorithm.
His most cited work include:
- Improved Steiner tree approximation in graphs (530 citations)
- An 11/6-approximation algorithm for the network steiner problem (275 citations)
- Tighter Bounds for Graph Steiner Tree Approximation (272 citations)
What are the main themes of his work throughout his whole career to date?
Alexander Zelikovsky spends much of his time researching Computational biology, Algorithm, Combinatorics, Steiner tree problem and Genetics.
His Computational biology research incorporates elements of RNA-Seq, Genome, DNA microarray, DNA sequencing and Haplotype.
His Discrete mathematics research extends to the thematically linked field of Combinatorics.
His Minimum spanning tree study in the realm of Discrete mathematics connects with subjects such as k-minimum spanning tree.
Alexander Zelikovsky combines subjects such as Approximation algorithm and Graph with his study of Steiner tree problem.
His Approximation algorithm research is multidisciplinary, incorporating elements of Time complexity, Routing and Heuristics.
He most often published in these fields:
- Computational biology (26.41%)
- Algorithm (16.02%)
- Combinatorics (14.84%)
What were the highlights of his more recent work (between 2017-2021)?
- Computational biology (26.41%)
- Antibody (6.82%)
- DNA sequencing (12.46%)
In recent papers he was focusing on the following fields of study:
His main research concerns Computational biology, Antibody, DNA sequencing, Data science and Inference.
His Computational biology study combines topics from a wide range of disciplines, such as Hepatitis C virus, RNA, Outbreak, Single cell sequencing and Haplotype.
His Antibody research incorporates themes from Lyme disease, Virology and Acquired immune system.
His Data science study which covers Grand Challenges that intersects with Tumour heterogeneity and Data integration.
His research in Inference intersects with topics in Selection, Cluster analysis, Contextual image classification, Phylogenetics and Fitness landscape.
His work in Artificial intelligence covers topics such as Genome which are related to areas like Algorithm.
Between 2017 and 2021, his most popular works were:
- Eleven grand challenges in single-cell data science (187 citations)
- Eleven grand challenges in single-cell data science (187 citations)
- Systematic benchmarking of omics computational tools (57 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Gene
- Computer network
His primary areas of study are Data science, Computational biology, Grand Challenges, Benchmarking and Genome.
His Data science study integrates concerns from other disciplines, such as Maximum likelihood and Field.
His studies in Computational biology integrate themes in fields like Acquired immune system, Viral quasispecies, Hepatitis C virus, Outbreak and Deep sequencing.
His Viral quasispecies research also works with subjects such as
- DNA sequencing most often made with reference to Big data,
- Inference, which have a strong connection to Genomic data.
His Grand Challenges research is multidisciplinary, relying on both Data integration and Tumour heterogeneity.
He has researched Genome in several fields, including Sequence assembly, Matching, Artificial intelligence, Source code and Machine learning.
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