November 11, 2020

May 07, 2020

Milana Frenkel-Morgenstern Zoom Meeting

May 05, 2020

Milana Frenkel-Morgenstern Zoom Meeting

February 07, 2020

You are cordially invited to attend the “Common Mechanism in Alzheimer’s diseases and T2D uncovered by Big Data and Deep Learning” workshop at The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel, on March 3-4, 2020.
As part of a new initiative to culture an international network for The Azrieli Faculty of Medicine, we most warmly welcome your participation in this inaugural event of an international series of workshops, sponsored by the DELL/EMC, Data Science Institute, Batsheva de Rothschild Fund, Azrieli Faculty of Medicine, and Dangoor Institute of Personalized Medicine at the Bar-Ilan University.
In this two day workshop, we are bringing together scientists from the cutting edge of the scientific fields of neuro-degenerative diseases and Type 2 diabetes. The workshop includes sponsored receptions and lunches, a tour of the ancient city of Safed (optional), as well as visits to the Faculty of Medicine laboratories.

Among the speakers –

  • Prof. Michal Schwartz, Weizmann Institute of Science, Israel
  • Prof. Michal Beeri, Sheba Medical Center, Israel
  • Prof. Tamir Tuller, Tel Aviv University, Israel
  • Prof. Judith Aharon-Perez, Rambam Medical Center, Israel
  • Dr. Kathleen Curran, University College Dublin, Ireland
  • Dr. Michael Galperin, NCBI, USA.

Please submit your posters before February 25th, 2020,
or oral presentations before February 5th, 2020

For more information and registration please visit the workshop’s website:

February 06, 2020

Frenkel-Morgenstern lab from Bar-Ilan University’s Azrieli Faculty of Medicine are taking part in the global Pan-Cancer Project to create a huge resource of primary cancer genomes:

Israeli scientists assist unprecedented mapping of cancer genome

Científicos israelíes contribuyen a mapeo sin precedentes del genoma del cáncer

Genomic basis for RNA alterations in cancer. Nature. volume 578, pages 129–136(2020).
Pan-cancer analysis of whole genomes. Nature. volume 578, pages 82–93(2020).

August 19, 2019

The interview Dr. Milana Frenkel-Morgenstern on Доброе утро, Израиль (to see a full version of the program click here):

July 28, 2019

Tagore, S., Gorohovski, A., Jensen, L.J., Raviv-Shay, D., Frenkel-Morgenstern, M. (2019)
New Tool Mines Scientific Texts for Fusion Protein Facts

A new computational tool called ProtFus screens scientific literature to validate predictions about the activity of fusion proteins—proteins encoded by the joining of two genes that previously encoded two separate proteins. Frenkel-Morgenstern lab at the Bar-Ilan University, Israel, present ProtFus in PLOS Computational Biology.

ProtFus tutorial

ProtFus is able to identify fusion proteins that may go by multiple names, and it can identify experimentally verified interactions between fusion proteins and other proteins. When applied to a test set of 1,817 fusion proteins, ProtFus identified 2,908 interactions across 18 cancer types that had been published in scientific texts from PubMed.

June 17-19, 2019 in San Francisco, California

Dr. Frenkel-Morgenstern presented her presentation “Diagnosis of Low Burden Tumors Using Circulating Cell-Free DNA” at The Liquid Biopsy Summit, June 17-19, 2019 in San Francisco, California.

June 5, 2019

Interview “Liquid Biopsy And Art In Science: Future Directions” to Diagnostics News has been published:

Liquid biopsy can diagnose any disease, says Milana Frenkel-Morgenstern, and is often an essential first step. For some of our most serious cancer types, when effective treatment is not yet available, early diagnosis is crucial.
Moving forward, personalized biomarkers will help, and the Frenkel-Morgenstern lab has built protein networks to detect personalized drug targets.
Frenkel-Morgenstern is head of the Cancer Genomics and BioComputing of Complex Diseases Lab at Bar-Ilan University. On behalf of Diagnostics World News, Hannah Loss recently interviewed Frenkel-Morgenstern, and explored how her work is informed by both bioinformatics and cancer genomics, the link between art and science, and more.

Diagnostics World News: You are trained in bioinformatics from the Weizmann Institute of Science and focused on both this topic and cancer genomics in your post-doctoral studies at the Spanish National Cancer Research Center (CNIO) in Madrid. How have these research areas informed your work on cell-free DNA in liquid biopsy?

Milana Frenkel-Morgenstern: Yes, indeed. I have been trained in Bioinformatics and Cancer Genomics, and I received very strong analytic tools to explore medical sciences. I was very excited to learn about liquid biopsy for prenatal diagnostics and then started to explore this field in cancer research.
When I arrived at the Azrieli Faculty of Medicine at the Bar-Ilan University in 2015, I decided to work in the field of liquid biopsy, integrating both computational and experimental tools. I immediately established both wet and dry labs. We are thus able to offer our national and international students a unique opportunity to be involved in interdisciplinary projects.

Please explain why diagnosing a glioma tumor type is an essential step for correct treatment.

This is a very dangerous tumor type and effective treatment is not yet available. Therefore, appropriate early diagnostics is needed, in addition to the development of personalized treatment for these tumors.
We identified unique personalized biomarkers, which we recently explored. We have built protein networks to detect personalized drug targets. This constitutes a very promising means of targeting this dangerous disease.

How can liquid biopsy improve this diagnosis process? Are there applications beyond tumor detection?

Liquid biopsy is an effective way to improve the diagnosis process because the cell- free DNA is derived from any part of the tumor and produces biomarkers that reflect the heterogeneity of the tumor. We aim to improve methods of biomarker detection, and also to explore various personalized biomarkers in timely and effective ways. We need to develop means of investigating protein-protein interaction networks to explore cellular processes affected in cancers using biomarkers from liquid biopsies. Next, we need to develop the capability of identifying drug targets, probably using deep machine learning, in efficient personal approaches.

In addition to your research, you are also the founder of the Art in Science competition at the Intelligent Systems for Molecular Biology (ISMB) conferences. What connections do you see between art and the field of cell-free diagnostics?

Yes, I founded and have organized the Art in Science competition since 2008. I think the connection between art and science is very important, particularly, for popular sciences. Art produces visual stereotypes for science. For example, the way we imagine the DNA helix and the way we see cells, these entail artistic representations of scientific discoveries. I think we need to develop ways to visualize “circulating cell-free DNA”, “liquid biopsy”, and “personalized treatment” that will encourage more people to explore these matters, and promote more creative solutions in an interdisciplinary way.

From your perspective, what must be solved before liquid biopsies can be implemented for broader routine clinical applications? What will it take to solve this?

Liquid biopsy can specifically diagnose any disease. We recently developed liquid biopsy for Alzheimer’s disease, arthritis, lupus, and Parkinson’s disease, in addition to various cancer types. We have observed that liquid biopsy is able to make very precise predictions and is a useful way of early diagnostics of complex diseases for diverse patients. We are seeking sponsors and investors to support our work as it is very time demanding and requires considerable computational and experimental resources, particularly for next-generation deep sequencing. We look forward to recruiting new PhD students to join us in the field of liquid biopsy.

Jul 7, 2017

Frenkel-Morgenstern, M., Gorohovski, A., Tagore, S., Sekar, V., Vazquez, M., Valencia, A. (2017)
ChiPPI: A Novel Method for Mapping Chimeric Protein-Protein Interactions Uncovers Selection Principles of Protein Fusion Events in Cancer.Nucleic Acids Res. 45(12): 7094-7105. (Impact Factor – 10.12)

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

Jan 4, 2017

Gorohovski, A., Tagore, S., Palande, V., Malka, A., Raviv-Shay, D., Frenkel-Morgenstern, M. (2017)
ChiTaRS-3.1 – the enhanced Chimeric Transcripts and RNA-seq Database matched with Protein-Protein Interactions.Nucleic Acids Res. 45(D1): D790-D795 (Impact Factor – 10.12)

Discovery of chimeric RNAs, which are produced by chromosomal translocations as well as the joining of exons from different genes by trans-splicing, has added a new level of complexity to our study and understanding of the transcriptome. The enhanced ChiTaRS-3.1 database ( is designed to make widely accessible a wealth of mined data on chimeric RNAs, with easy-to-use analytical tools built-in. The database comprises 34,922: chimeric transcripts along with 11,714: cancer breakpoints. In this latest version, we have included multiple cross-references to GeneCards, iHop, PubMed, NCBI, Ensembl, OMIM, RefSeq and the Mitelman collection for every entry in the ‘Full Collection’. In addition, for every chimera, we have added a predicted chimeric PROTEIN-PROTEIN INTERACTION CHIPPI NETWORK: , which allows for easy visualization of protein partners of both parental and fusion proteins for all human chimeras. The database contains a comprehensive annotation for 34,922: chimeric transcripts from eight organisms, and includes the manual annotation of 200: sense-antiSense (SaS) chimeras. The current improvements in the content and functionality to the ChiTaRS database make it a central resource for the study of chimeric transcripts and fusion proteins.

May 23, 2016

The interview with Prof. Carasso on the Israeli Channel 10 (to see a full version of the program click here )

The interview with Prof. Carasso on the Israeli Channel 10

Nov 8 – Nov 12, 2015

There were the Literature Text-Mining Approach in Cancer Research course. This is a new international course sponsored by the Danish Agency of Science as a part of the Israel-Danish collaboration of Dr. Frenkel-Morgenstern and Prof. Jensen. Your participation opened a new chapter in text-mining and Cancer Research. We invited scientists at the cutting edge of the text-mining and bioinformatics.

Such course, to our knowledge, has not been done before at the Bar-Ilan University, and we hope that it will generate a fruitful interaction between the international students and scientists.

The Literature Text Mining Approach In Cancer Research