We make use of large-scale biomedical data, such as genetics, genomics, transcriptomics and biological knowledge bases, and data mining algorithms to build applications for biomedical research.
A particular interest of the group is to develop and combine machine learning and network analysis of biological data enabling:
- Biomarker and drug target discovery for precision medicine.
- Integrative analysis of omics and biological knowledge network for exploration of disease mechanisms and ‘molecular actions’ of drugs/compounds.
- Data-driven discovery of disease subtypes/endotypes and identification of related biomarkers.
- Classification models using omics-driven features for in silico prediction of efficacy and/or toxicity in drug discovery.
We have expertise in developing tools for omics data analytics, network and data mining algorithms, and artificial intelligence-based methods, including machine learning.
Ongoing research project
- Academy project: Artificial Intelligence based strategies to efficiently mine big biomedical data (including patient-level data) and quickly assess combinations of biomarkers from large-scale omics and clinical data.
- Jane and Aatos Erkko Foundation: Development of an AI-based system for classifying patients into subpopulations that differ in their susceptibility to a particular cancer type or subtype and for systematic pre-clinical screening of potential drug combinations.
- BIOMAP: Development of analytical tools and methods for multi-omics modelling, subtype discovery and biomarker identification.
- EDCMET: Toxicogenomic-based approach to assess metabolic effects of endocrine disrupting chemicals.
Vittorio Fortino, Academy Research Fellow, Associate Professor (tenure-track).
- Dr Luca Cattelani. Project manager and senior researcher studying ML-driven solutions for computer-aided biomarker discovery (AoF). Email: firstname.lastname@example.org
- Dr Alireza Zourmand. Project manager and senior researcher studying ML-driven solutions for GWAS studies. Email: email@example.com
- Teemu Rintala. Early stage researcher developing ML methods for omics-driven subtype patient discovery (BIOMAP). Email: firstname.lastname@example.org
- Arindam Ghosh. Early stage researcher focusing on systems biology and bioinformatics tools for drug target discovery. Email: email@example.com.
- Dr Amirhossein Sakhteman. Project researcher combining toxicogenomic data and machine learning methods for in silico evaluation of EDC-induced toxicity. Email: firstname.lastname@example.org
Metabolic Diseases RC
Previous group members
- Dr Amirhossein Sakhteman (2019/2020). Project researcher working on machine learning methods for in silico evaluation of EDC-induced toxicity.
- Dr Mario Failli (2018/2019). Post-doctoral researcher working on network data mining algorithms for drug target prioritization based on efficacy and safety estimates.
- Olli Ukkonen (2018/2019). Early stage researcher developing the computational infrastructure for the PharmAI project.