I am working on the development and application of computational approaches to tackle emerging problems in cancer research. I have experience in the analysis of cancer genomic (NGS) data from liquid biopsies, in identifying and characterizing genomic alterations, with applications in cancer therapeutics and precision oncology. In my most recent projects I am also using CyTOF and IMC datasets to characterise cellular features with single cell-resolution.

During my research career, I have successfully applied different machine learning techniques in biomarker discovery studies, in gene regulation studies, and in identifying and characterising DNA regulatory elements such as enhancers, promoters and microRNAs. I have also particular expertise in big data integration from multi-omics technologies (genomic, transcriptomic, proteomic and epigenomic), and I am specialist in facilitating biological discoveries using high performance computing (HPC) systems. I have worked in different institutes worldwide, and I have established collaborations with scientists with multidisciplinary scientific backgrounds.


  • 2022 - Lecturer of the course BINF200, Analysis of biological sequences and structures, Department of Informatics, University of Bergen, Norway
  • 2021 - Lecturer of the course Genomics for Precision Medicine, Topic: Introduction to DNA-Seq processing for cancer data – SNV detection and interpretation, Organised by NORBIS, Norway
  • 2020 - Lecturer of cancer research courses, organised by the Centre for Cancer Biomarkers (CCBIO), University of Bergen, Norway. Topics:
    1. CCBIO905: Computational methods for the analysis of mass cytometry imaging data
    2. CCBIO906: Computational methods for copy number variation detection and data interpretation
  • 2014-2015 Teaching assistant of MSc/PhD courses of the Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Kingdom of Saudi Arabia
Academic article
  • Show author(s) (2022). Identifying predictors of survival in patients with leukemia using single-cell mass cytometry and machine learning. bioRxiv (preprint article).
  • Show author(s) (2022). Early response evaluation by single cell signaling profiling in acute myeloid leukemia. Research Square.
  • Show author(s) (2022). Development of an antibody panel for imaging mass cytometry to investigate cancer-associated fibroblast heterogeneity and spatial distribution in archival tissues. bioRxiv (preprint article).
  • Show author(s) (2020). Genome-wide plasma DNA methylation features of metastatic prostate cancer. Journal of Clinical Investigation.
  • Show author(s) (2020). Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing. Scientific Reports.
  • Show author(s) (2020). Circulating Tumour DNA Sequencing Identifies a Genetic Resistance-Gap in Colorectal Cancers with Acquired Resistance to EGFR-Antibodies and Chemotherapy. Cancers.

More information in national current research information system (CRIStin)


  1. Zhu G, Guo A, [...], Kleftogiannis D, [...] and Anders Skanderup, Tissue-specific cell-free DNA degradation quantifies circulating tumor DNA burden, Nature Communications, (2021), doi: https://doi.org/10.1038/s41467-021-22463-y
  2. Knebel F, Barber L , Newey A, Kleftogiannis D, [...] and Gerlinger M, Circulating Tumour DNA Sequencing Identifies a Genetic Resistance-Gap in Colorectal Cancers with Acquired Resistance to EGFR-Antibodies and Chemotherapy, Cancers, (2020),doi: 10.3390/cancers12123736.
  3. Kleftogiannis D, Ho D, […], and Ng S, Detection of genomic alterations in breast cancer with circulating free DNA sequencing, Scientific Reports, (2020), doi: 10.1038/s41598-020-72818-6.
  4. Wu A, Cremaschi P, […], Kleftogiannis D, […] and Attard G, The plasma methylome of metastatic prostate cancer, Journal of Clinical Investigation, (2020), doi: 10.1172/JCI130887.
  5. Massoti C, Knebel F, […], Kleftogiannis D and Bettoni F, Detection of ESR1 mutations in plasma cell-free DNA from metastatic ER-positive breast cancer patients resistant to hormone therapy, Clinical Cancer Research, AACR, (2020), doi: 10.1158/1557-3265.LiqBiop20-A19
  6. Yogev O, Almedia G, [...], Kleftogiannis D, [...], and Chesler L, In vivo modelling of chemo-resistant neuroblastoma provides new insights into chemo-refractory disease and metastatic progression, Cancer Research, (2019) ,doi:10.1158/0008-5472.CAN-18-2759.
  7. Kleftogiannis D, Punta M, […], Lise S, Identification of single nucleotide variants using position-specific error estimation in deep sequencing data, BMC Medical Genomics, (2019), doi: 10.1186/s12920-019-0557-9.
  8. Kleftogiannis D, Ashoor A, Bajic VB. TELS: a novel computational framework for identifying motif signatures of transcribed enhancers, Genomics, Proteomics & Bioinformatics, (2018), doi.org/10.1016/j.gpb.2018.05.003.
  9. Mansukhani S, Barber L, Kleftogiannis D, […] and Gerlinger M, Ultra-sensitive mutation detection and genome-wide DNA copy number reconstruction by error corrected circulating tumor DNA sequencing, Clinical Chemistry (2018), doi: 10.1373/clinchem.2018.289629.
  10. Kleftogiannis D, Kalnis P, Arner E, Bajic VB. Discriminative identification of promoters and enhancers transcriptional responses after stimulus, Nucleic Acids Research (2016), doi: 10.1093/nar/gkw1015.
  11. Kleftogiannis D, Kalnis P, Bajic VB. Progress and challenges in bioinformatics approaches for enhancer identification, Briefings in Bioinformatics (2015), doi:  10.1093/bib/bbv101.
  12. Ashoor A, Kleftogiannis D, Radovanovic A, Bajic VB. DENdb: Database of integrated human enhancers, Database: The journal of Biological Database and Curation (2015), doi:10.10.93/database/bav085.
  13. Kleftogiannis D, Wong L, Archer JA, Kalnis P. Hi-Jack: a novel computational framework for pathway-based inference of host-pathogen interactions, Bioinformatics (2015), doi: 10.1093/bioinformatics/btv138.
  14. Soufan O, Kleftogiannis D, Kalnis P, Bajic VB. DWFS: Feature selection with a parallel genetic algorithm. PloS ONE (2015), doi: 10.1371/journal.pone.0117988.
  15. Kleftogiannis D, Theofilatos K, Lykothanasis S, Mavroudi S. YamiPred: A novel evolutionary method for predicting pre-miRNAs and selecting relevant features, IEEE/ACM Transactions on Computational Biology and Bioinformatics (2015), doi: 10.1109/TCBB.2014.2388227.
  16. Korfiati A, Theofilatos K, Kleftogiannis D, et al. Predicting Human miRNA Target Genes Using a Novel Computational Intelligent Framework, Information Sciences (2015), doi: 10.1016/j.ins.2014.09.016.
  17. Karathanou K, Theofilatos K, Kleftogiannis D, et al. ncRNAclass: A Web Platform for Non-Coding RNA Feature Calculation and MicroRNAs and Targets Prediction, International Journal on Artificial Intelligence Tools (2015), https://doi.org/10.1142/S0218213015400023.
  18. Kleftogiannis D, Kalnis P, Bajic VB, DEEP: A general computational framework for predicting enhancers, Nucleic Acids Research (2014), doi: 10.1093/nar/gku1058.
  19. Rapakoulia T, Theofilatos K, Kleftogiannis D, et al. EnsembleGASVR: A novel ensemble method for classifying missense Single Nucleotide Polymorphisms, Bioinformatics (2014), doi: 10.1093/bioinformatics/btu297.
  20. Theofilatos K, Dimitrakopoulos C, Likothanassis S, Kleftogiannis D, et al. The Human Interactome Knowledge Base (HINT-KB): an integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique, Artificial Intelligence Review (2014), doi: 10.1007/s10462-013-9409-8.
  21. Kleftogiannis D, Korfiati A, Theofilatos K, et al. Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role. Journal of Biomedical Informatics (2013), doi: 10.1016/j.jbi.2013.02.002.
  22. Kleftogiannis D, Kalnis P, Bajic VB. Comparing Memory-Efficient Genome Assemblers on Stand-Alone and Cloud Infrastructures. PLoS ONE (2013), doi:10.1371/journal.pone.0075505.
  23. Kleftogiannis D, Theofilatos K, Papadimitriou S, et al. ncRNA-Class web tool: non-coding RNA feature extraction and pre-miRNA classification web tool, Artificial Intelligence Applications and Innovations (2012), doi:10.1007/978-3-642-33412-2_65.

My main project is titled "Improved Treatments of Acute Myeloid Leukaemias by Personalised Medicine (AML_PM)" funded by ERAPerMed.

It is a joint collaborative effort between University of Bergen (CBU and Helse Bergen Haukeland University Hospital), University of Groningen , German Cancer Research Center (DKFZ), University of Freiburg, and Princess Margaret Cancer Centre in Canada. 

I am also involved in the following projects where I contribute bioinformatics analyses:

  • 2021 - University of Bergen, Project: “Hormone regulators and immune landscape in breast cancer of the young – signature biomarkers for improved diagnosis and outcome”, Funder: Helse Vest.
  • 2021 - University of Bergen, Project: “Nerve involvement in breast cancer”, Funder: Norwegian Cancer Research Society.
  • 2019 - Institute of Cancer Research (ICR) – Royal Marsden Cancer Hospital, Project:
    “CANCEREVO: Deciphering and predicting the evolution of cancer cell populations”, Funder: ERC funded (Consolidator Grant)
  • 2018 - National Cancer Centre Singapore (SingHealth), Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Project: “CaLiBRe: Cancer Liquid Biopsy for Real-time diagnostics and early intervention”, Funder: A*STAR (National Liquid Biopsy program)