Research groups

is an EMBL Group Leader at (FIMM), which is an independent unit of (HiLIFE). 

research group has expertise in network-centric and machine learning-based approaches to modeling and predicting complex relationships between genetic dependencies and medical phenotypes such as susceptibility to diseases and responses to treatments. We are using both 'reverse-genetic' approaches, including RNAi, CRISPR and drug screening, as well as 'forward-genetic' approaches, such as GWAS and next-generation sequencing studies. Combining functional and genetic profiling will provide a more comprehensive network view of the mechanisms behind disease processes and enable accurate predictions of system-level phenotypic responses to genetic and chemical perturbations.

is professor of Systems Biology at the and head of the research group.

The focus of the  group is to understand and find effective means to overcome drug resistance in cancers. Our approach is to use systems biology, i.e., integration of large and complex molecular & clinical data (big data) from cancer patients with computational methods and wet lab experiments, to identify efficient patient-specific therapeutic targets.

A particular interest of the group is to develop and apply machine learning based methods that enable integration of various types of molecular data (DNA, RNA, proteomics, etc.) to clinical information. All our research is done in collaboration with oncologists, pathologists, biochemists and geneticians with the aim of translating medical data into predictions and clinical benefits.

Professor Liisa Holm is a head of Bioinformatics group at the Department of Biosciences and at the . The group's main interests are protein sequence-structure-function relationships and computational analysis of gene regulation.

is a university lecturer in computer science and heads the research group . The group develops efficient algorithms and data structures for dealing with large scale sequential data.

is a group leader at . The group develops methods for evolutionary sequence analysis and studies evolutionary processes using genome-scale population resequencing data.

Ville Mustonen is a professor of bioinformatics at the Department of Biosciences, Department of Computer Science and Institute of Biotecnology. The group is part of Foundations of Computational Health Research Programme at Helsinki Institute for Information Technology and of International Cancer Genome Consortium’s Pan-Cancer project.

The group focuses on big data and the opportunities it creates for biology and medicine. We develop computational algorithms to discover and understand functionally relevant genetic and phenotypic variation. We work with systems of direct relevance to human health, for example, in the context of cancer and infectious disease and evolution of drug resistance. The work is collaborative and cross-disciplinary. We have a record of successful research collaborations working together with clinicians and experimentalists.

is a professor of computer science and heads a research group on . 

We develop algorithms and data structures for the analysis of genome-scale data. Such data is abundant due to modern molecular biology measurement techniques like high-throughput sequencing. We are especially interested in applications of compressed data structures, that make it possible to analyse the often highly redundant data within the space of their information content. Our latest developments focus on pan-genome indexing, space-efficient sequence analysis, alignments on sequence-like structures, and transcript and genome assembly.

is an Academy Research Fellow at the and head of the research group Algorithms for Biological Sequencing Data. The group's main interests are in developing algorithms for genome assembly.