What is he best known for?
The fields of study he is best known for:
Christian von Mering focuses on Computational biology, Genome, String, Bioinformatics and Genetics.
His research in Computational biology tackles topics such as Annotation which are related to areas like Pairwise comparison, GeneCards and Phylogenetics.
Genome is a subfield of Gene that Christian von Mering studies.
His research integrates issues of Interaction network, Disparate system, Data integration and Information retrieval in his study of Bioinformatics.
Christian von Mering carries out multidisciplinary research, doing studies in Genetics and Set.
His Interaction information study combines topics from a wide range of disciplines, such as Hierarchical clustering, Feature, Data mining and Upload.
His most cited work include:
- STRING v10: protein–protein interaction networks, integrated over the tree of life (5620 citations)
- STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. (3363 citations)
- The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (3344 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Genetics, Computational biology, Genome, Gene and Genomics.
His Computational biology study integrates concerns from other disciplines, such as Annotation, DNA microarray, Identification and Taxonomic rank.
His Genome research is multidisciplinary, incorporating perspectives in Evolutionary biology, Cancer, Proteome and Metagenomics.
Christian von Mering studied Gene and Data mining that intersect with Inference.
Christian von Mering interconnects Phylogenetics and Gene ontology in the investigation of issues within Genomics.
Christian von Mering performs multidisciplinary study in the fields of String and Bioinformatics via his papers.
He most often published in these fields:
- Genetics (41.21%)
- Computational biology (28.48%)
- Genome (28.48%)
What were the highlights of his more recent work (between 2018-2021)?
- Genome (28.48%)
- Gene (28.48%)
- Cancer (12.12%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Genome, Gene, Cancer, Genomics and Data mining.
His research in Genome is mostly focused on Whole genome sequencing.
He undertakes multidisciplinary studies into Cancer and String in his work.
The various areas that Christian von Mering examines in his Genomics study include Germline mutation, Phylogenetics and Computational biology.
His study looks at the relationship between Computational biology and topics such as Genetic algorithm, which overlap with Metagenomics.
His research investigates the link between Data mining and topics such as Inference that cross with problems in DNA microarray, Tree, Consistency and Structural biology.
Between 2018 and 2021, his most popular works were:
- STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. (3363 citations)
- Pan-cancer analysis of whole genomes (538 citations)
- eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. (410 citations)
In his most recent research, the most cited papers focused on:
His main research concerns Genome, Genomics, Computational biology, Whole genome sequencing and Metagenomics.
His Genome research integrates issues from Biotinylation, DNA methylation, Protein domain, RNA Splicing Factors and Chromatin remodeling.
His Genomics research incorporates elements of Point mutation, Germline mutation, Massive parallel sequencing and Germline.
His Computational biology research includes elements of Taxonomic rank, Proteome, Phylogenetics and Genetic algorithm.
Research on Genetics and Gene is a part of his Whole genome sequencing study.
As a member of one scientific family, he mostly works in the field of Metagenomics, focusing on Scalability and, on occasion, Data mining.
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