Portrait of Dr. Annalisa Marsico

Principle Investigator, ICB

Dr. rer. nat. Annalisa Marsico

“My mission is to decode the language of RNA and its networks across space and time to unlock a new generation of RNA therapies, using explainable AI to transform how we treat currently untreatable diseases.”

Visit my Lab  

Academic Career and Research Areas

Annalisa Marsico's scientific journey began at the intersection of physics, biology and AI. Trained as a theoretical physicist at the University of Bari, Italy, she pivoted to bioinformatics with a Master's in Siena and a doctorate at TU Dresden within the Max Planck IMPRS program — an interdisciplinary path that gave her a necessary quantitative rigour applied to biological questions. Postdoctoral work at the Max Planck Institute for Molecular Genetics in Berlin cemented her focus on RNA and computational genomics.

In 2014, she started her first independent group at the Max Planck Institute for Molecular Genetics while simultaneously holding a Junior Professorship for High-Throughput Genomics at Freie Universität Berlin — a dual role she sustained for four years, building expertise in RNA bioinformatics, machine learning for biological sequences, and teaching. 

Since 2019, she has led the Computational RNA Biology group at Helmholtz Munich's Computational Health Center. Her research sits at the frontier of AI, RNA biology, and graph modeling. Her work advances the integration of experimentally grounded RNA foundation models with network-based approaches to decode RNA–protein regulation and RNA function in health and disease, enabling applications from non-coding mutation interpretation to RNA therapeutic design. She continuously develops sequence-to-function models for molecular interactions in, e.g. viral infections, and pioneered graph convolutional networks for disease gene prioritization across cancer, with current applicaitons to neurological disorders. More recently, she developed AI frameworks leveraging biomedical knowledge graphs for tasks ranging from disease understanding to drug repurposing and non-coding RNA regulation. She envisions the future in combining sequence and network modalities through agentic AI to accelerate therapeutic discovery. In collaboration with industry partners such as Boehringer Ingelheim, Roche, and Ethris, she contributes to RNA biomarker discovery and therapeutic design, with the goal of developing RNA drugs for orphan diseases.

Fields of Work and Expertise

RNA Biology

RNA Therapeutics

Language Models

Interpretable AI

Biomedical Knowledge Graphs

Professional Background

Since 2019

Group leader of the Computational RNA Biology group, Helmholtz Munich, Germany

2017

Successful evaluation of Junior Professorship, equivalent to the German Habilitation in Bioinformatics at Freie Universität Berlin

2014 - 2019

Joint appointment as junior professor for High-throughput Genomics Free University of Berlin and group leader at the Max Planck Institute for Molecular Genetics Berlin, Germany

2010 - 2014

Postdoc, Max Planck Institute for Molecular Genetics Berlin, Germany

2009

Doctorate, Technical University of Dresden, Joint PhD in bioinformatics inside the IMPRS Max Planck graduate school

Honors and Awards

  • 2024 - Received a call for a W3 professorship in "Computational Life Sciences" at RWTH Aachen University, declined.
  • 2012 - Award from the Max Planck Society, Berlin, from the workshop series "Career building for excellent female postdocs" (€15,000 personal award).
  • 2005 - PhD fellowship from the Max Planck IMPRS graduate school, Dresden.
  • 2004 - Master's fellowship from the Italian Ministry of Education, Universities and Research, Siena.

Recent Publications

Nat. Bio. Eng., DOI: 10.1038/s41551-025-01598-z (2026)

Hu, E.Y. ; Oleshko, S. ; Firmani, S. ; Cheng, H. ; Zhu, Z. ; Ulmer, M.A. ; Arnold, M. ; Colomé-Tatché, M. ; Tang, J. ; Xhonneux, S. ; Marsico, A.

Enhancing link prediction in biomedical knowledge graphs with BioPathNet.
Bioinformatics 41:btaf491 (2025)

An, Y. ; Bergant, V. ; Grünke, C. ; Bonnal, B. ; Henrici, A. ; Pichlmair, A. ; Schubert, B. ; Marsico, A.

TransFactor-Prediction of pro-viral SARS-CoV-2 host factors using a protein language model.
iScience 28:113342 (2025)

Rehawi, G. ; Hagenberg, J. ; Sämann, P.G. ; Moyon, L. ; Binder, E. ; List, M. ; Marsico, A. ; Knauer-Arloth, J.

Integrative gene and isoform co-expression networks reveal regulatory rewiring in stress-related psychiatric disorders.
Nat. Commun. 16:4733 (2025)

Schmerer, N. ; Janga, H. ; Aillaud, M. ; Hoffmann, J. ; Aznaourova, M. ; Wende, S. ; Steding, H. ; Halder, L.D. ; Uhl, M. ; Boldt, F. ; Stiewe, T. ; Nist, A. ; Jerrentrup, L. ; Kirschbaum, A. ; Ruppert, C. ; Rossbach, O. ; Ntini, E. ; Marsico, A. ; Valasarajan, C. ; Backofen, R. ; Linne, U. ; Pullamsetti, S.S. ; Schmeck, B. ; Schulte, L.N.

A searchable atlas of pathogen-sensitive lncRNA networks in human macrophages.
Genome Res. 34, 572-589 (2024)

Iyer, D.P. ; Moyon, L. ; Wittler, L. ; Cheng, C.Y. ; Ringeling, F.R. ; Canzar, S. ; Marsico, A. ; Bulut-Karslioğlu, A.

Combinatorial microRNA activity is essential for the transition of pluripotent cells from proliferation into dormancy.
Cell 186, 4834-4850.e23 (2023)

Schmidt, N. ; Ganskih, S. ; Wei, Y. ; Gabel, A. ; Zielinski, S. ; Keshishian, H. ; Lareau, C.A. ; Zimmermann, L. ; Makroczyova, J. ; Pearce, C. ; Krey, K. ; Hennig, T. ; Stegmaier, S. ; Moyon, L. ; Horlacher, M. ; Werner, S. ; Aydin, J. ; Olguin-Nava, M. ; Potabattula, R. ; Kibe, A. ; Dölken, L. ; Smyth, R.P. ; Caliskan, N. ; Marsico, A. ; Krempl, C. ; Bodem, J. ; Pichlmair, A. ; Carr, S.A. ; Chlanda, P. ; Erhard, F. ; Munschauer, M.

SND1 binds SARS-CoV-2 negative-sense RNA and promotes viral RNA synthesis through NSP9.
NAR Gen. Bioinfo. 5:lqad026 (2023)

Salvatore, M. ; Horlacher, M. ; Marsico, A. ; Winther, O. ; Andersson, R.

Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility.

Micolucci, L. ; Matacchione, G. ; Albertini, M.C. ; Marra, M. ; Ramini, D. ; Giuliani, A. ; Sabbatinelli, J. ; Procopio, A.D. ; Olivieri, F. ; Marsico, A. ; Monsurrò, V.

A data-mining approach to identify NF-kB-responsive microRNAs in tissues involved in inflammatory processes: Potential relevance in age-related diseases.

Building a better world?

Join our team.