What is he best known for?
The fields of study he is best known for:
Eckart Meese mainly investigates microRNA, Molecular biology, Genetics, Gene and Bioinformatics.
His research in microRNA intersects with topics in Cancer, Gene expression profiling, Microarray, Biomarker and Regulation of gene expression.
His study in Gene expression profiling is interdisciplinary in nature, drawing from both DNA microarray, Disease, Human genetics and DNA sequencing.
His Molecular biology study integrates concerns from other disciplines, such as CCR1, XCL2, Alternative splicing, Complementary DNA and Fusion protein.
His research investigates the link between Gene and topics such as Virology that cross with problems in Human endogenous retrovirus, Human endogenous retrovirus K and Chromosome 7.
His work in Bioinformatics addresses issues such as Computational biology, which are connected to fields such as Candidate Disease Gene, Gene regulatory network and MiRBase.
His most cited work include:
- Distribution of miRNA expression across human tissues (442 citations)
- Recombination: Multiply infected spleen cells in HIV patients. (373 citations)
- New insights into the Tyrolean Iceman's origin and phenotype as inferred by whole-genome sequencing (325 citations)
What are the main themes of his work throughout his whole career to date?
Eckart Meese spends much of his time researching microRNA, Genetics, Gene, Molecular biology and Cancer research.
His studies in microRNA integrate themes in fields like Bioinformatics, Gene expression profiling, Disease, Regulation of gene expression and Computational biology.
His Bioinformatics research incorporates themes from Biomarker, Cancer and DNA microarray.
His research integrates issues of Microarray and Lung cancer, Pathology in his study of Cancer.
His work carried out in the field of Gene brings together such families of science as Cell culture and Cell.
His biological study spans a wide range of topics, including Fluorescence in situ hybridization, Locus, DNA and Gene mapping.
He most often published in these fields:
- microRNA (36.61%)
- Genetics (25.68%)
- Gene (24.32%)
What were the highlights of his more recent work (between 2017-2021)?
- microRNA (36.61%)
- Computational biology (14.21%)
- Gene (24.32%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in microRNA, Computational biology, Gene, Cell biology and RNA.
His microRNA research includes elements of Microarray, Gene expression, Phenotype, Disease and Regulation of gene expression.
His Gene expression study combines topics from a wide range of disciplines, such as Blood cell and Cancer research, Angiogenesis.
His Computational biology research also works with subjects such as
- MiRBase that intertwine with fields like Sequencing data and Workflow management system,
- Sequence analysis and related Human genome.
His Gene study necessitates a more in-depth grasp of Genetics.
His RNA study combines topics in areas such as Biomarker and Transcriptome.
Between 2017 and 2021, his most popular works were:
- An estimate of the total number of true human miRNAs. (139 citations)
- MicroRNA in diagnosis and therapy monitoring of early-stage triple-negative breast cancer. (67 citations)
- A comprehensive profile of circulating RNAs in human serum. (56 citations)
In his most recent research, the most cited papers focused on:
Eckart Meese focuses on microRNA, Computational biology, Cell biology, Gene and MiRBase.
The various areas that he examines in his microRNA study include Microarray, Gene expression, Candidate Disease Gene and Disease.
He has included themes like Cancer and Oncology in his Disease study.
His work deals with themes such as Regulation of gene expression, Genome, Sequence analysis and Data set, which intersect with Computational biology.
His Gene research is multidisciplinary, incorporating elements of Sperm, Andrology and Binding site.
His work investigates the relationship between MiRBase and topics such as Sequencing data that intersect with problems in Biological data, Molecular Sequence Annotation and Human cell.
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