Computational Systems Medicine in Cancer

Tero Aittokallio
Group leader
Introducing the group's aims (video)

Our group has expertise in integrating multi-omics profiling and clinical information from cancer patients using mathematical and statistical approaches such as machine learning and network modeling. The medical aim is to optimize treatment outcomes for individual patients using maximally predictive models and minimal biomarker signatures that enable real-time and cost-effective routine diagnostics and prognosis. We believe that combining functional, molecular and genomic profiling information is critical for next-generation precision medicine applications, where integrative modeling and clever use of big data will pinpoint effective and selective targets for personalized therapies.

Selected Publications

  1. Ianevski A, Giri AK, Aittokallio T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nature Communications 2022; 13: 1246. http://doi.org/10.1038/s41467-022-28803-w.
  2. CichoĊ„ska A, Ravikumar B, Allaway RJ, Wan F, Park S, Isayev O, Li S, Mason M, Lamb A, Tanoli Z, Jeon M, Kim S, Popova M, Capuzzi S, Zeng J, Dang K, Koytiger G, Kang J, Wells CI, Willson TM; IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium, Oprea TI, Schlessinger A, Drewry DH, Stolovitzky G, Wennerberg K, Guinney J, Aittokallio T. Crowdsourced mapping of unexplored target space of kinase inhibitors. Nature Communications 2021;12:3307. doi: 10.1038/s41467-021-23165-1.
  3. Akimov Y, Bulanova D, Timonen S, Wennerberg K, Aittokallio T. Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics. Molecular Systems Biology 2020; 16: e9195. doi.org/10.15252/msb.20199195
  4. Ianevski A, Lahtela J, Javarappa KK, Sergeev P, Ghimire BR, Gautam P, Vähä-Koskela M, Turunen L, Linnavirta N, Kuusanmäki H, Kontro M, Porkka K, Heckman CA, Mattila P, Wennerberg K, Giri AK, Aittokallio T.  Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Science Advances 2020; 7: eabe4038, DOI: 10.1126 / sciadv.abe4038
  5. Ianevski A, Giri AK, Gautam P, Kononov A, Potdar S, Saarela J, Wennerberg K, Aittokallio T. Prediction of drug combination effects with a minimal set of experiments. Nature Machine Intelligence 2019; 1: 568–577. doi.org/10.1038/s42256-019-0122-4 PDF copy

We are funded by the following organizations