What is he best known for?
The fields of study he is best known for:
His primary areas of investigation include Genetics, Computational biology, Inparanoid, Gene and Genome.
In general Genetics study, his work on Sequence analysis, Nucleic acid sequence and Gene expression profiling often relates to the realm of Nematode and Cell cycle checkpoint, thereby connecting several areas of interest.
Erik L. L. Sonnhammer conducts interdisciplinary study in the fields of Computational biology and Rfam through his research.
His Inparanoid research integrates issues from Sequence homology, Phylogenetics, Pairwise comparison and UniProt.
Erik L. L. Sonnhammer has included themes like Lineage, Phylogenetic tree, Cluster analysis, Protein family and Helminth genetics in his Genome study.
The various areas that Erik L. L. Sonnhammer examines in his Transmembrane protein study include Integral membrane protein, Protein structure prediction, Fungal protein and Topology.
His most cited work include:
- The Pfam protein families database (13194 citations)
- Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes (8874 citations)
- Pfam: the protein families database. (3852 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Computational biology, Genetics, Gene, Data mining and Gene regulatory network.
His Computational biology research includes themes of Domain, Gene expression profiling, Protein domain, Function and Genomics.
His Protein domain study combines topics in areas such as Protein function prediction and Homology.
His research integrates issues of Bioinformatics, Software, Multiple sequence alignment, Benchmark and Sequence in his study of Data mining.
His studies in Gene regulatory network integrate themes in fields like Biological network, Inference and Crosstalk.
His Sequence homology research focuses on Proteome and how it connects with Transmembrane protein.
He most often published in these fields:
- Computational biology (43.72%)
- Genetics (35.52%)
- Gene (27.32%)
What were the highlights of his more recent work (between 2017-2021)?
- Computational biology (43.72%)
- Gene regulatory network (18.03%)
- Inference (18.58%)
In recent papers he was focusing on the following fields of study:
Erik L. L. Sonnhammer mostly deals with Computational biology, Gene regulatory network, Inference, Gene regulatory network inference and Genome.
His work deals with themes such as Repetitive Sequences, Protein Families Database, Cancer gene, Homology and Gene duplication, which intersect with Computational biology.
His Protein Families Database research incorporates themes from Sequence Ontology, Proteins metabolism, Protein methods and UniProt Knowledgebase.
The Gene regulatory network study combines topics in areas such as Data type, Information transfer and Phylogenetic tree.
His Inference research is multidisciplinary, relying on both SIGNAL, Gene and Synthetic data.
His Genome research incorporates elements of Field and State.
Between 2017 and 2021, his most popular works were:
- The Pfam protein families database in 2019. (1886 citations)
- Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity (124 citations)
- Pfam: The protein families database in 2021. (55 citations)
In his most recent research, the most cited papers focused on:
Erik L. L. Sonnhammer mainly investigates Computational biology, Protein Families Database, Repetitive Sequences, Gene family and Genome.
His Computational biology research includes themes of Data type, Information transfer, Phylogenetic tree, Genomics and Homology.
The Protein Families Database study combines topics in areas such as Proteins metabolism, Protein methods and UniProt Knowledgebase.
His Repetitive Sequences study combines topics from a wide range of disciplines, such as Functional annotation, Sequence Ontology and Molecular Sequence Annotation.
His work on Model organism expands to the thematically related Gene family.
His research in Genome intersects with topics in Metagenomics and Protein family.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
