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.
- PhosFox: a bioinformatics tool for peptide-level processing of LC-MS/MS-based phosphoproteomic data (2014)
- Proteome Science
(A1 Refereed original research article in a scientific journal) - Phosphoproteomics Combined with Quantitative 14-3-3-affinity Capture Identifies SIRT1 and RAI as Novel Regulators of Cytosolic Double-stranded RNA Recognition Pathway (2014)
- Molecular and Cellular Proteomics
(A1 Refereed original research article in a scientific journal) - Predicting binding affinities between drug compounds and kinase targets (2014) The eighth International Workshop on Machine Learning in Systems Biology Anna Cichonska, Tapio Pahikkala, Antti Airola, Juho Rousu, Tero Aittokallio
(Other publication) - Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapiesRegularized Machine Learning in the Genetic Prediction of Complex Traits (2014)
- Scientific ReportsPLoS Genetics
(A1 Refereed original research article in a scientific journal) - (2014)
(A2 Refereed review article in a scientific journal ) - Statistical detection of quantitative protein biomarkers provides insights into signaling networks deregulated in acute myeloid leukemia (2014)
- Proteomics
(A1 Refereed original research article in a scientific journal) - Stroma- Derived Factors Significantly Impact the Drug Response Profiles of Patient- Derived Primary AML Cells: Implications for Drug Sensitivity Testing (2014)
- Blood
(Other publication) - Systematic high-throughput drug sensitivity and resistance testing (DSRT) of ovarian cancer cell lines indicates novel therapeutic possibilities with existing and emerging drugs (2014)
- Cancer Research
(Other publication) - Anticancer compound ABT-263 accelerates apoptosis in virus-infected cells and imbalances cytokine production and lowers survival rates of infected mice (2013)
- Cell Death and DiseaseClinical Cancer Research
(A1 Refereed original research article in a scientific journal) - Characterization of primary high-grade serous ovarian cancer cell lines: Cell line and growth condition specific differences in stem cell marker expression and high-throughput drug screening (2013)
(Other publication)



