Tero Aittokallio
PhD
teanai@utu.fi Tykistökatu 6 A Turku ORCID identifier: https://orcid.org/orcid.org/0000-0002-0886-9769 |
Tero Aittokallio received his PhD in Applied Mathematics from the University of Turku in 2001, under the supervision of Prof. Mats Gyllenberg. He then did his post-doctoral training in the Systems Biology Lab at the Institut Pasteur (2006-2007), with Dr. Benno Schwikowski, where he focused on network biology applications using high-throughput experimental assays and network analysis tools such as Cytoscape. In 2007, Dr. Aittokallio launched his independent career as a principal investigator in the Turku Biomathematics Research Group, where he received a five-year appointment as an Academy of Finland Research Fellow (2007-2012). Tero Aittokallio joined Institute for Molecular Medicine Finland (FIMM) as EMBL Group Leader in the fall of 2011, and was selected as Professor of Statistics and Applied Mathematics at University of Turku in 2015.
Aittokallio's research group focuses on developing and applying integrated computational-experimental approaches to tackle biomedical questions, such as how genes function as interaction networks to carry out and regulate cellular processes, how alterations in these networks contribute to complex traits, such as human diseases, and where and how in the disease network one should target to optimally inhibit the disease phenotypes, such as tumor growth.
Computational statistics.
Scientific computing.
- Integrated analysis of drug sensitivity and selectivity to predict synergistic drug combinations and target coaddictions in cancer (2019) Systems Chemical Biology : Methods and protocols Jaiswal Alok, Yadav Bhagwan, Wennerberg Krister, Aittokallio Tero
(A3 Refereed book chapter or chapter in a compilation book) - JAK/STAT-Activating Genomic Alterations Are a Hallmark of T-PLL (2019)
- Cancers
(A1 Refereed original research article in a scientific journal) - Machine learning and feature selection for drug response prediction in precision oncology applications (2019)
- Biophysical Reviews
(A2 Refereed review article in a scientific journal ) - Matrix and Tensor Factorization Methods for Toxicogenomic Modeling and Prediction (2019) Advances in Computational Toxicology Suleiman A. Khan, Tero Aittokallio, Andreas Scherer, Roland Grafström, Pekka Kohonen
(A3 Refereed book chapter or chapter in a compilation book) - Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer (2019)
- npj Systems Biology and Applications
(A1 Refereed original research article in a scientific journal) - Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy (2019)
- Nature Communications
(A1 Refereed original research article in a scientific journal) - Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets (2019)
- Cell Chemical Biology
(A1 Refereed original research article in a scientific journal) - Prediction of drug combination effects with a minimal set of experiments (2019)
- Nature machine intelligence
(A1 Refereed original research article in a scientific journal) - Adrenals Contribute to Growth of Castration-Resistant VCaP Prostate Cancer Xenografts (2018)
- American Journal of Pathology
(A1 Refereed original research article in a scientific journal) - Aggressive natural killer-cell leukemia mutational landscape and drug profiling highlight JAK-STAT signaling as therapeutic target (2018)
- Nature Communications
(A1 Refereed original research article in a scientific journal)



