# Artemis G. Hatzigeorgiou
**Artemis G. Hatzigeorgiou** is a bioinformatician who works on computational analysis of microRNAs (miRNAs), the short non-coding RNAs that regulate gene expression after transcription. According to her DIANA lab personal page, she is known for creating and leading the DIANA tool suite, including the DIANA-microT target-prediction algorithm, the DIANA-TarBase database of experimentally supported miRNA–gene interactions, and the DIANA-miRPath functional analysis server. In 2019 she became Professor at the Department of Computer Science and Biomedical Informatics of the University of Thessaly, where she became head of DIANA-Lab.[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis) • [2](http://www.microrna.gr/dianalab/)
| Key facts | |
|---|---|
| Field | Bioinformatics; computational miRNA analysis |
| Current position | Professor, Dept. of Computer Science and Biomedical Informatics, University of Thessaly (elected 2019); head of DIANA-Lab from 2019[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis) • [2](http://www.microrna.gr/dianalab/) |
| Training | MSc Computer Science, University of Stuttgart; PhD Molecular Biology, University of Jena, 2001[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis) |
| Earlier posts | Assistant Professor of Bioinformatics, University of Pennsylvania (2001–2007); PI, Institute of Molecular Oncology, B.S.R.C. "Alexander Fleming" (2007–2012)[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis) |
| Signature work | "A combined computational-experimental approach predicts human microRNA targets", *Genes & Development*, 2004 (DIANA-microT)[3](https://genesdev.cshlp.org/content/18/10/1165) |
| Known for | DIANA-microT (2003), DIANA-TarBase (2006), DIANA-miRPath[4](http://microrna.gr/hatzigeorgiou) • [5](https://pmc.ncbi.nlm.nih.gov/articles/PMC1370898/) |
| Award | NSF Early Career Award, 2003[4](http://microrna.gr/hatzigeorgiou) |
## Education and early career
Hatzigeorgiou studied Computer Science at the University of Stuttgart, where she received an MSc, and earned a PhD in Molecular Biology from the Department of Biology and Pharmacy at the University of Jena in 2001.[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis)
Before her doctoral work she contributed to two early software ventures. She is co-author of the Stuttgart Neural Network Simulator (SNNS), an open-source package for simulating artificial neural networks that has been used worldwide, and she has been co-founder since 1996 of Synaptic Ltd, a computer science company located in Herakleion, Crete.[4](http://microrna.gr/hatzigeorgiou)
## Career record
Her positions, with dates as her faculty page records them:[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis)
- **2001–2007:** Assistant Professor of Bioinformatics at the University of Pennsylvania, with a joint appointment in the Department of Genetics and a secondary appointment in the Department of Computer and Information Science. In 2007 she was elected adjunct professor in the same CIS department.[4](http://microrna.gr/hatzigeorgiou)
- **2007–2012:** Principal Investigator at the Institute of Molecular Oncology of the Biomedical Sciences Research Center "Alexander Fleming" in Vari, Greece.[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis)
- **2012–2019:** Professor of Bioinformatics at the Department of Electrical and Computer Engineering, University of Thessaly; in 2019 she was elected Professor at the Department of Computer Science and Biomedical Informatics, where she holds her current chair.[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis)
- **Since 2015:** Collaborating Professor at the Hellenic Pasteur Institute in Athens, which remains a DIANA-Lab collaborator and a co-affiliation on her recent papers.[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis) • [6](https://doi.org/10.1093/nar/gkad431)
She chaired the 17th European Conference on Computational Biology (ECCB18) and became President of the Hellenic Society for Computational Biology and [Bioinformatics](https://www.edgepedia.org/bioinformatics). She has also served as a reviewer panelist for the Wellcome Trust Sanger Institute, the NSF, the NIH, the European Union, and the Greek General Secretariat of Research and Technology.[1](https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis) Current work in her group is supported by a grant from the Hellenic Foundation for Research and [Innovation](https://www.edgepedia.org/innovation) (Project Number 2563).[7](https://doi.org/10.1152/physiol.2024.39.s1.2046)
## Representative work
The 2004 *Genes & Development* paper described DIANA-microT, a computational program that identifies mRNA targets for animal miRNAs and predicts human and mouse targets bearing single miRNA-recognition elements (MREs).[3](https://genesdev.cshlp.org/content/18/10/1165)
## The DIANA suite
**DIANA-microT**, released in 2003, was one of the first published [miRNA target prediction](https://www.edgepedia.org/mirna-target-prediction) programs.[4](http://microrna.gr/hatzigeorgiou) According to the DIANA-microT 2023 update in *Nucleic Acids Research*, its microT-CDS version has served the community since 2009 and was among the first algorithms to predict miRNA binding sites in both the 3′ untranslated region and the coding sequence.[8](https://doi.org/10.1093/nar/gkad283) The 2023 update yields about 67 million predicted interactions across human, mouse, rat, chicken, fly, and worm, and adds about 2 million predicted interactions for miRNAs from about 16 viruses against human, mouse, and chicken host transcripts; the current web server hosts more than 86 million interactions with the exact genomic locations of the predicted MREs.[8](https://doi.org/10.1093/nar/gkad283) • [9](https://dianalab.e-ce.uth.gr/microt_webserver/)
**DIANA-TarBase**, founded in 2006 at the University of Pennsylvania Center for Bioinformatics, was built as a manually curated collection of experimentally tested miRNA targets in human and mouse, fruit fly, worm, and zebrafish, distinguishing positive from negative results.[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC1370898/) • [10](https://ngdc.cncb.ac.cn/databasecommons/database/id/21) The current version, TarBase v9.0, catalogs about 6 million entries comprising about 2 million unique miRNA–gene pairs, supported by 37 experimental protocols across 172 tissues and cell-types, covering more than 3300 miRNAs and more than 40,000 genes over 24 species, and it introduces virally encoded miRNAs and target-directed miRNA degradation (TDMD) events.[11](https://pmc.ncbi.nlm.nih.gov/articles/PMC10767993/)
**DIANA-miRPath v4.0** (2023) tailors target-based miRNA functional analysis to specific biological and experimental contexts, with GO, KEGG, REACTOME, MSigDB, and PFAM enrichment, integration of TCGA, GTEx, and single-cell expression datasets, and a CRISPR knock-out screen module.[6](https://doi.org/10.1093/nar/gkad431)
## How the tools compare
Independent reviews place DIANA-microT among the usable standards of the field. A 2016 comparative review judged TargetScan the most robust sequence-based prediction tool, while noting that the miTG score of DIANA-microT and the mirSVR score of miRanda complement it by taking additional biological parameters into account.[12](https://www.mdpi.com/1422-0067/17/12/1987) A 2014 review singled out DIANA-microT-CDS, miRanda-mirSVR, and TargetScan for ease of use, reliance on relatively updated miRBase versions, and range of capabilities.[13](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2014.00023/full) Earlier work showed that DIANA-microT 3.0 achieved precision comparable to TargetScanS and PicTar while predicting largely non-overlapping target sets, only about 40% of its predicted elements shared with PicTar and about 48% with TargetScan 4.2; because it accepts non-conserved MREs it can also predict targets of viral miRNAs that conservation-dependent algorithms miss.[14](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-295) A machine-learning variant, DIANA-microT-ANN, trained an artificial neural network on proteomics data measuring protein repression after miRNA overexpression, and for 542 human miRNAs it predicted 120,000 targets not provided by TargetScan 5.0.[15](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2011.00103/full) A 2025 benchmark using single-cell miRNA–mRNA co-sequencing data found that in mouse primary cells DIANA-microT led seven widely used algorithms, closely followed by miRmap, while in human cell lines it gave excellent results alongside TargetScan, miRmap, and miRDB, with mirDIP performing best; performance also depends on how many targets a user considers.[16](https://www.biorxiv.org/content/10.1101/2025.06.23.661076v1.full-text)
## Honors and recognition
Hatzigeorgiou received the Early Career Award from the [National Science Foundation](https://www.edgepedia.org/national-science-foundation) of the USA in 2003, while at the University of Pennsylvania.[4](http://microrna.gr/hatzigeorgiou) The DIANA servers she leads are registered reference resources; TarBase, for example, is listed in the Database Commons registry with her as contact PI, hosted in Greece by the University of Thessaly.[10](https://ngdc.cncb.ac.cn/databasecommons/database/id/21)
## References
1. Hatzigeorgiou Artemis – Department of Computer Science and Biomedical Informatics, University of Thessaly. https://dib.uth.gr/?lang=en&personnel=hatzigeorgiou-artemis
2. DIANA-Lab. http://www.microrna.gr/dianalab/
3. A combined computational-experimental approach predicts human microRNA targets. *Genes & Development*, 2004. https://genesdev.cshlp.org/content/18/10/1165
4. Prof. Artemis Hatzigeorgiou – DIANA lab personal page. http://microrna.gr/hatzigeorgiou
5. TarBase: A comprehensive database of experimentally supported animal microRNA targets. *RNA*, 2006. https://pmc.ncbi.nlm.nih.gov/articles/PMC1370898/
6. DIANA-miRPath v4.0: expanding target-based miRNA functional analysis in cell-type and tissue contexts. *Nucleic Acids Research*, 2023. https://doi.org/10.1093/nar/gkad431
7. Pathway analysis for functional characterization of microRNAs. American Physiology Summit 2024 abstract. https://doi.org/10.1152/physiol.2024.39.s1.2046
8. DIANA-microT 2023: including predicted targets of virally encoded miRNAs. *Nucleic Acids Research*, 2023. https://doi.org/10.1093/nar/gkad283
9. DIANA-microT Webserver (official lab site). https://dianalab.e-ce.uth.gr/microt_webserver/
10. DIANA-TarBase – Database Commons record. https://ngdc.cncb.ac.cn/databasecommons/database/id/21
11. TarBase-v9.0 extends experimentally supported miRNA–gene interactions to cell-types and virally encoded miRNAs. *Nucleic Acids Research*, 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC10767993/
12. Tools for Sequence-Based miRNA Target Prediction: What to Choose? *IJMS*, 2016. https://www.mdpi.com/1422-0067/17/12/1987
13. Common features of microRNA target prediction tools. *Frontiers in Genetics*, 2014. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2014.00023/full
14. Accurate microRNA target prediction correlates with protein repression levels. *BMC Bioinformatics*, 2009. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-295
15. Accurate microRNA target prediction using detailed binding site accessibility and machine learning on proteomics data. *Frontiers in Genetics*, 2011. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2011.00103/full
16. Benchmarking microRNA Target Prediction Algorithms Using Single-Cell Co-Sequencing Data. bioRxiv, 2025. https://www.biorxiv.org/content/10.1101/2025.06.23.661076v1.full-text
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*Topic: Encyclopedia › Life and health › Life and health scientists › Life scientists › Researchers in computational biology, bioinformatics, and systems biology › Genomics and transcriptomics*
*Initially written Sep 21, 2026 · Reviewed: Sep 25, 2026 · Edited: Sep 25, 2026 · Last review: Sep 25, 2026*
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License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license