Tero Aittokallio
PhD
teanai@utu.fi Tykistökatu 6 A Turku |
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.
- Matched preclinical designs for improved translatability (2017)
- Science Translational MedicineEuropean Journal of Human Genetics
- Orphan G protein-coupled receptor GPRC5A modulates integrin β1-mediated epithelial cell adhesion (2017)
- Cell Adhesion and MigrationCancer Research
- Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data (2017)
- Lancet Oncology
- (2017)
- OncotargetClinical Cancer Research
- Re: Fatemeh Seyednasrollah, Mehrad Mahmoudian, Liisa Rautakorpi, et al. How Reliable are Trial-based Prognostic Models in Real-world Patients with Metastatic Castration-resistant Prostate Cancer? Eur Urol. 2017;71:838-40Comprehensive drug testing of patient derived conditionally reprogrammed cells from castration -resistant prostate cancer (2017)
- European UrologyCancer Research
- Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells (2017)
- Genome MedicineNature
- SynergyFinder: a web application for analyzing drug combination dose-response matrix data (2017)
- Bioinformatics
- (2017)
- Gynecologic OncologyBioinformatics
- Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression (2017)
- BioinformaticsNature Communications
- The inconvenience of data of convenience: computational research beyond post-mortem analyses2017
- Nature MethodsMolecular Cancer
- Whole-genome view of the consequences of a population bottleneck using 2926 genome sequences from Finland and United Kingdom (2017)
- Accumulated Metabolites of Hydroxybutyric Acid Serve as Diagnostic and Prognostic Biomarkers of Ovarian High-Grade Serous Carcinomas (2016)
- Cancer stem cell drugs target K-ras signaling in a stemness context (2016)
- Oncogene
- Characterization of ascites and tumor-derived ovarian cancer stem-like cells (2016)
- (2016)
- Consistency in drug response profiling (2016)
- Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis (2016)
- Nature Communications
- Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization (2016)
- Erratum: Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis (2016)
- Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells (2016)