Head of Bioinformatics and Omics Data Science Platform
Berlin Institute for Medical Systems Biology (BIMSB)
Max Delbrück Center (MDC)
Hannoversche Straße 28
Phone: +49 30 9406 4271
Fax: +49 30 9406 49341
Our lab is focused on using genomics and machine learning to discover new diagnostics and therapeutics, with a particular emphasis on oncology. We use state-of-the-art techniques to analyze genetic data and identify key markers for disease diagnosis and treatment. Our goal is to improve patient outcomes and advance the field of precision medicine. One of our main areas of focus is on target discovery for solid tumors. We are using cutting-edge machine learning techniques to identify new targets, drugs, and biomarkers for cancer therapy. Additionally, we are also developing diagnostic tests for cardiovascular diseases using machine learning algorithms and genomic data to improve early detection and personalized treatment of these conditions. Since 2018, we have published numerous papers, filed a patent, been awarded translational grants, and field-tested our technologies on industry collaborations.
We have also been active in the field of computational tool development. We developed more than a dozen software tools for genomic data analysis. The tools cover a larger section of computational genomics from data processing for RNA-seq, single-cell genomics, ChIP-seq, ATAC-seq etc., to statistics and machine learning applications. We value the reproducibility and documentation of our software tools and regularly release high-quality data analysis software.
- Cuadrat, R.R.C., Kratzer, A., Arnal, H.G., Rathgeber, A.C., Wreczycka, K.,Blume, A., Gündüz, I.B., Ebenal, V., Mauno, T., Osberg, B., Moobed, M., Hartung, J., Jakobs, K., Seppelt, C., Meteva, D., Haghikia, A.,Leistner, D.M., Landmesser, U., Akalin, A. (2023) "Cardiovascular disease biomarkers derived from circulating cell-free DNA methylation." NAR Genom Bioinform. 2023 Jun 28;5(2):lqad061. doi: 10.1093/nargab/lqad061. PMID: 37388821; PMCID:PMC10304763.
- Dohmen, J., Baranovskii, A., Ronen, J., Uyar, B., Franke, V., Akalin, A.(2022) "Identifying tumor cells at the single-cell level using machine learning." Genome Biology. 2022 May 30;23(1):123. doi.org/10.1186/s13059-022-02683-1
- Akalin, A.(2020) “Computational Genomics with R”. Chapman and Hall/CRC, 2020 (Book). doi.org/10.1201/9780429084317
- Kopp, W., Monti, R., Tamburrini, A., Ohler, U., Akalin, A.(2020) "Deep learning for genomics using Janggu." Nature Communications. 2020 Jul 13;11(1):3488. doi.org/10.1038/s41467-020-17155-y
- Ronen, J., Hayat, S., Akalin, A.(2019) "Evaluation of colorectal cancer subtypes and cell lines using deep learning." Life Science Alliance. 2019 Dec 2;2(6):e201900517. doi.org/10.26508/lsa.201900517