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.

1. Willoch-Allen K, Zhao Z, Ayuda-Durán P, Mikaeloff F, Struyf N, Österroos A, Lehmann S, Kallioniemi O, Saarela J, Tyner JW, Erkers T, Enserink JM & Aittokallio T. A multi-center study on the consistency of drug sensitivity testing in patients with acute myeloid leukemia. NPJ Precision Oncology. 2026
2. Zobolas J, George AM, López A, Fischer S, Becker M, Aittokallio T. Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data. BioData Mining. 2026
3. Romero-Becerra R, Zhao Z, Nebdal D, Müller E, Bergholtz H, Norum JH & Aittokallio T.Improved analysis of in vivo drug combination experiments with a comprehensive statistical framework and web-tool. Nature Communications. 2025
4. Zhao Z, Zobolas J, Zucknick M, Aittokallio T. Tutorial on survival modeling with applications to omics data. Bioinformatics. 2024
5. Chen Y, He L, Ianevski A, Ayuda-Durán P, Potdar S, Saarela J, Miettinen JJ, Kytölä S, Miettinen S, Manninen M, Heckman CA, Enserink JM, Wennerberg K & Aittokallio T. Robust scoring of selective drug responses for patient-tailored therapy selection. Nature Protocols. 2024
6. Zhao Z, Wang S, Zucknick M & Aittokallio T. Tissue-specific identification of multi-omics features for pan-cancer drug response prediction. iScience. 2022
7. 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
8. 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. 2021