What is she best known for?
The fields of study she is best known for:
MicroRNA, Computational biology, Gene, Genetics and Bioinformatics are her primary areas of study.
Artemis G. Hatzigeorgiou frequently studies issues relating to Regulation of gene expression and microRNA.
Her Ensembl research extends to Computational biology, which is thematically connected.
Her work on Gene expression, Coding region, Alternative splicing and Synteny as part of her general Gene study is frequently connected to Microchromosome, thereby bridging the divide between different branches of science.
Her work on Evolutionary biology expands to the thematically related Genetics.
Her Bioinformatics research is multidisciplinary, incorporating perspectives in Web server, In silico and Mirna target.
Her most cited work include:
- Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution (2193 citations)
- microRNAs exhibit high frequency genomic alterations in human cancer (961 citations)
- DIANA-miRPath v3.0: deciphering microRNA function with experimental support (787 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are microRNA, Computational biology, Genetics, Gene and Gene expression.
Her research in microRNA intersects with topics in RNA, Transcription factor, Function, Messenger RNA and Regulation of gene expression.
Her work carried out in the field of Computational biology brings together such families of science as Non-coding RNA, Web server, In silico and Bioinformatics.
Her research on Genetics focuses in particular on Translation.
Her research on Gene often connects related areas such as Molecular biology.
Her research integrates issues of Transcription and Exon in her study of Gene expression.
She most often published in these fields:
- microRNA (64.38%)
- Computational biology (55.48%)
- Genetics (28.08%)
What were the highlights of her more recent work (between 2018-2021)?
- Computational biology (55.48%)
- microRNA (64.38%)
- Cap analysis gene expression (11.64%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Computational biology, microRNA, Cap analysis gene expression, Locus and RNA splicing.
The concepts of her Computational biology study are interwoven with issues in MiRBase, Non-coding RNA and Transfer RNA.
The study incorporates disciplines such as MiRNA binding, Regulation of gene expression, Sequence analysis and Search engine indexing in addition to Non-coding RNA.
Artemis G. Hatzigeorgiou undertakes interdisciplinary study in the fields of microRNA and Mechanism through her research.
Her research on Cap analysis gene expression also deals with topics like
- Gene expression, Transcriptional noise, Benchmark, Machine learning and Intron most often made with reference to Transcription,
- Artificial intelligence that intertwine with fields like Algorithm, Gene regulatory network, Identification and Gene expression profiling.
Artemis G. Hatzigeorgiou works mostly in the field of Function, limiting it down to concerns involving In vitro and, occasionally, Gene and Cell biology.
Between 2018 and 2021, her most popular works were:
- RNAcentral: a hub of information for non-coding RNA sequences (82 citations)
- DIANA-LncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts. (25 citations)
- DIANA-LncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts. (25 citations)
In her most recent research, the most cited papers focused on:
Artemis G. Hatzigeorgiou mainly focuses on Computational biology, Non-coding RNA, RNA splicing, Exon and Sequence analysis.
Her Computational biology study combines topics in areas such as RNA, Transfer RNA and Gene.
The various areas that Artemis G. Hatzigeorgiou examines in her Non-coding RNA study include Annotation, Genome browser, Genome and Nucleic acid.
Her RNA splicing research incorporates themes from Gene expression, Identification, Algorithm, Transcriptional noise and Gene isoform.
Her Exon research is multidisciplinary, incorporating elements of Artificial intelligence, Benchmark, Locus, Cap analysis gene expression and Machine learning.
Her biological study spans a wide range of topics, including MiRNA binding, Regulation of gene expression and Search engine indexing.
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