The goal of the focus area is to develop theory and computational methods for complex data and systems in medicine and healthcare. We focus on scaling up computation for large numbers of genomes, machine learning methods for medical big data, as well as complex network modelling and mining.  We collaborate with top medical research groups and hospitals to bring our tools towards practical use in healthcare decision support systems.

Research Groups

Genome-Scale Algorithmics (Focus Area Leader, group leader: Veli Mäkinen, University of Helsinki)

Machine Learning for Health (Aalto-ML4H) (Focus Area Deputy Leader, group leader: Pekka Marttinen, Aalto University)

Kernel Methods, Pattern Analysis and Computational Metabolomics (KEPACO) (HIIT Deputy Director, group leader: Juho Rousu, Aalto University)

Medical Image Computing (group leader: Koen Van Leemput, Aalto University)

Institute for Molecular Medicine Finland (FIMM) (group leader: Tero Aittokallio, University of Helsinki)

Bioinformatics and Evolution (group leader: Ville Mustonen, University of Helsinki)

Complex Systems (group leader: Jari Saramäki, Aalto University)


Bioinformatics afternoon: https://www2.helsinki.fi/en/researchgroups/bioinformatics/bioinformatics-afternoon

IDG-Dream challenge: https://www.hiit.fi/idg-dream-challenge/

Shonan meeting on computational metabolomics and machine learning: https://shonan.nii.ac.jp/seminars/179/


An ERC Starting Grant was awarded to Dr. Alexandru Tomescu to develop  fundamental algorithmic techniques with applications to genome assembly, RNA assembly and pan-genome analysis. https://www.helsinki.fi/en/news/higher-education-policy/two-erc-starting-grants-awarded-university-helsinki

The University of Helsinki joins a Europe-wide research project which focuses on algortihms utilised in pangenomic analysis.  The goal of the project is to look for more efficient ways of presenting the masses of data accumulated through genome sequencing which will make it easier to utilise this data, for example, in treating diseases. https://www.helsinki.fi/en/faculty-science/news/university-helsinki-joins-europe-wide-research-project-focused-algorithms-utilised-pangenomic-analysis

Heli Julkunen developed a machine learning method that predicts how different drug combinations kill cancer cells, and now she helps advance preventive healthcare as a data scientist. https://www.aalto.fi/en/news/if-you-study-computer-science-you-may-end-up-developing-better-cancer-treatments-by-applying

Research Highlights

Algorithmic Foundations of Pangenomics

String algorithms have been in vital role in the genome analysis tasks enabled by high-throughput sequencing data and the human reference genome. Recent trend in genomics is to use variation graphs to represent the genome content of a sample of individuals from the population (so-called pangenome). In our work, we have studied algorithmic foundations of pangenomics, extending string algorithms to work on graphs and related representations. Our seminal work on extending the Burrows-Wheeler transform to graphs (Sirén, Välimäki, Mäkinen, 2014) has created a new theory branch of Wheeler graphs, and it has influenced the design of popular software tools (vg, hisat2) published in Nature Biotechnology. More recently, we have e.g. tailored sparse dynamic programming algorithms to variation graphs (Mäkinen et al., 2019), shown that exact string matching is as hard as approximate string matching on graphs (Equi et al. 2019), and developed linear time segmentation algorithms to construct compressed pangenome representations in the form of founder sequences and indexable founder block graphs (Norri et al. 2019; Mäkinen et al. 2020).

J. Sirén, N. Välimäki and V. Mäkinen. Indexing Graphs for Path Queries with Applications in Genome Research, in IEEE/ACM TCBB, 11(2):375-388, 2014. Earlier in WABI 2011.

A. Mäkinen, I. Tomescu, A. Kuosmanen, T. Paavilainen, T. Gagie, R. Chikhi. Sparse Dynamic Programming on DAGs with Small Width. ACM Trans. Algorithms 15(2): 29:1-29:21, 2019. Earlier in RECOMB 2018.

M. Equi, R. Grossi, V. Mäkinen, A. I. Tomescu. On the Complexity of String Matching for Graphs. In Proc. ICALP 2019: 55:1-55:15.

A. Mäkinen, B. Cazaux, M. Equi, T. Norri, A. I. Tomescu. Linear Time Construction of Indexable Founder Block Graphs. In Proc. WABI 2020, LIPIcs 172, 7:1-7:18, 2020.

Machine learning for small molecule identification

We have developed award-winning (CASMI competitions 2016 & 2017, http://casmi-contest.org/2017/index.shtml)  methods and tools for identifying and annotating small molecules from their LC-MS/MS spectra using machine learning. Our tools, developed in collaboration with Böcker group at University of Jena (CSI:FingerID, Dührkop et al. 2015; SIRIUS, Dührkop et al. 2019; CANOPUS, Dührkop et al. 2021) are widely used in the metabolomics community, e.g. our CSI:FingerID server has answered more than 120M metabolite identification queries to date.

Dührkop et al. 2015. Searching molecular structure databases with tandem mass spectra using CSI: FingerIDProceedings of the National Academy of Sciences, 112(41), pp.12580-12585.

Dührkop et al. 2019. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nature methods, 16(4), pp.299-302.

Dührkop, et al., 2021. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nature Biotechnology, 39(4), pp.462-471.

Improved pipeline for constructing functional brain networks from fMRI data

In complex network modelling and mining applied to neuroscience, we have made a significant push towards better and more informed modelling and interpretation of functional brain networks derived from fMRI time series. In a series of papers (Korhonen et al. 2017, Alakörkkö et al. 2017, Ryyppö et al. 2018, Triana et al. 2019) we have focused on the preprocessing and modelling pipeline and shown that i) commonly applied spatial smoothing has unexpected effects and may give rise to artefacts, ii) the common approach of defining RoIs for functional network construction also suffers from problems; RoIs may require a dynamic interpretation.

O Korhonen et al 2017. Consistency of regions of interest as nodes of fMRI functional brain networks. Network Neuroscience 1 (3), 254-274 (2017)

Alakörkkö et al. 2017. Effects of spatial smoothing on functional brain networks. European Journal of Neuroscience 46 (9), 2471-2480 (2017)

Ryyppö et al. 2018. Regions of Interest as nodes of dynamic functional brain networks. Network Neuroscience 2 (4), 513-535 (2018)

Triana et al. 2019. Effects of spatial smoothing on group-level differences in functional brain networks. Network Neuroscience 4 (3), 556-574 (2019)

Predicting drug combination responses in cancer and infectious diseases

We have developed new machine learning methods for high-throughput screening of drug combinations for cancer therapies, including cost-effective experimental design of pairwise combination matrices (DECREASE, Ianevski et. al, 2019) and accurate prediction of dose-response matrices in cancer cell lines (comboFM, Julkunen et al. 2020; comboLTR, Wang et al. 2021). Current research includes prediction of higher-order drug interaction effects using higher-order tensors, as well translating the pre-clinical model predictions to cancer patients using transfer learning.

Ianevski, et al. 2019. Prediction of drug combination effects with a minimal set of experiments. Nature machine intelligence, 1(12), pp.568-577.

Julkunen et al, 2020. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effectsNature communications11(1), pp.1-11.

Wang et al. 2021. Modeling drug combination effects via latent tensor reconstruction, Bioinformatics, Volume 37, Issue Supplement_1, July 2021, Pages i93–i101,

Eco-evolutionary control of pathogens

Predictability and control of evolving populations is an emerging topic of high scientific interest and vast translational potential in applications such as vaccine and therapy design. Here we have started to developed eco-evolutionary control theory. Successful control of eco-evolutionary trajectories hinges on the intrinsic dynamics of the target system and the control leverage that can be applied. For example, we have studied what are the key determinants of controllability and how to apply control to improve therapies against pathogens.

M. Lässig and V. Mustonen, 2020. Eco-evolutionary control of pathogensProceedings of the National Academy of Sciences117(33), pp.19694-19704.