The big data era in molecular biology has created exiting potential for novel biological discoveries, but also exciting challenges for computer scientists in data management, analysis, and interpretation. Especially exciting are the possibilities in combining omics data with medical images and phenotype variables. Therefore, in the next decades there will be developed sophisticated bioinformatics and machine learning methods and framework to analyze and explore the information in the integrated data. However, the dataset sizes and complexity require the development of novel infrastructure systems, analysis approaches, and data exploration tools targeted for such complex health datasets.

Our goal is to provide the systems, methods, and tools needed to analyze and interpret complex health datasets. Our research interests are threefold. First, build and experimentally evaluate infrastructure systems for bioinformatics and machine learning analyses. Second, apply bioinformatics, statistics, and machine learning methods for novel health data analyses. Third, build and evaluate data exploration and interpretation tools. All our research is interdisciplinary. We therefore combine experimental computer science with real problems, applications, and data obtained from our biomedical research collaborators.

We also contribute to research infrastructure development and operation, commercialization of our research, and many outreach activities.


We contribute to several large and small projects.

NOWAC and Translational Cancer Research

The Norwegian Woman and Cancer (NOWAC) biobank contains time series with questionnaire data from 170 000 women and more than 60 000 blood samples. The biobank is analyzed using several omics technologies including microarrays, RNA-seq, methylation, and mass spectrometry. The data is being analyzed by the Systems Epidemiology group lead by Professor Torkjel Sandanger. Our responsibility in the project is to build a backend for standardized data analysis pipelines, machine learning based data analysis, and a system for exploration and visualization of the analysis results. We are using these as building blocks to build a platform for swift exploration of the data under different epidemiological designs.

We are also collaborating with Professors Lill-Tove Rasmussen Busund and Tom Dønnem from the Translational Cancer Research Group on multi-level and multi-tissue analysis and clinical use of data from NOWAC.

We also work with Professor Geir F. Lorem on biomedical ethics with a focus on direct-to-consumer genomics tests.

High North Population Studies

We are members of the strategic initiative High North Population Studies at UiT that combines epidemiological research and computer science to collect, analyze, and utilize the data collected in population studies at UiT. Our contributions are methods to uncover complex cross-level interactions in large heterogeneous population-study datasets, a framework for exploration of metagenomics data integrated with host genomics and phenotype data, and developing and operating infrastructure for bioinformatics analyses on sensitive data. We are collaborating with Professor Anne-Sofie Furberg, Associate Professor Anne Merethe Hanssen and Professor Christopher Sivert Nielsen on analysis of data from the Fit Futures study.

The Tromsø Lung study has built a database with more than 36.000 lung sound recordings. The recordings are done as part of the Tromsø Study 7, which is an Epidemiological study that was started in 1974. The database will be used to provide educational and analysis services for lung sounds. Our contributions are methods for automated classification and similarity search for the sounds. This project is done in collaboration with Hasse Melbye at the Department of Community Medicine, University of Tromsø. The results from this project are further developed by the Medsensio AS company.

Center for New Antibacterial Strategies (CANS)

Center for New Antibacterial Strategies (CANS) at UiT is a large inter-disciplinary center with a research focus on antimicrobial resistance. We are responsible for the bioinformatics analyses.

SFI Centre for Visual Intelligence

We are partners of the new Visual Intelligence Centre for Research-based Innovation (SFI). The Visual Intelligence research focus is on complex imagery, and in particular on learning from limited data, context and dependencies, confidence and uncertainty, and explainability and reliability. The innovation areas are medical imaging, marine sciences, industry and energy, and remote sensing. We will work on digital pathology with Dr. Thomas Kilvær, Associate Professor Kajsa Møllersen, and Professor Lill-Tove Rasmussen Busund. We develop methods and tools for analysis of tumor infiltrating lymphocytes in standard diagnostic whole-tissue hematoxylin and eosin stained section slides. We have a strong focus on the clinical application of the methods and tools. In addition, we have an active role in data management and innovation activities (including NORA.startup) in the center.

We are also partners in the new UNN/UiT Senter for Pasientnær AI, where our focus is digital pathology and breast cancer screening.

Big pharmacoepidemiology

In collaboration with Associate Professor Kristian Svendsen we are building a big database of prescription and adverse drug reaction data. We use the database to develop new machine learning methods for adverse drug reaction detection and interpretation.

MLOps for historical registers

The Norwegian Historical Data Centre is responsible for transcribing, linking, and making available historical Norwegian documents. We are working with Hilde Sommerseth on machine learning based transcription of Norwegian handwritten census books. Our contributions are the development and operation of pipelines for data cleaning, model training, and automated transcription. These are used to produce the data in the registers.


In the air:bit air pollution project we have developed educational projects for use in Norwegian High Schools. This work is done in collaboration with Skolelaboratoriet i realfag og teknologi at UiT. We provide build instructions, programming guides, and a portal for data analysis and live visualization. Air:bit has been used by 13 high-school classes in Northern Norway.


The lab currently consist of:

Name Title Main project Homepage
Lars Ailo Bongo Professor Principal investigator Homepage, GitHub, and Bitbucket
Edvard Pedersen Associate Professor Co-PI Homepage and GitHub
Einar Holsbø Associate Professor Co-PI Homepage and GitHub
Vi Tran Associate Professor Co-PI (on maternity leave) -
Morten Grønnesby PhD student NOWAC Homepage and GitHub
Rafael Nozal Cañadas PhD student Population studies in the north GitHub
Nikita Shvetsov PhD student Visual intelligence Bitbucket
Rigmor Katrine Johansen Affiliated PhD student Biomedical ethics Homepage
Mohsen Askar Affiliated PhD student Big pharmacoepidemiology Homepage
Sidra Tahi Master student . .
Anton Garri Fagerbakk Master student . .
Bjørn-Richard Pedersen Affiliated scientific staff Population studies in the north GitHub
Wilhelm Vold Intern Training app GitHub
Bjørn Fjukstad Adjunct associate professor Industry mentor DIPS
Jonas Juselius Adjunct associate professor Industry mentor Serit IT-partner
Dominic Riley Adjunct advisor Industry mentor The Grow Room

Former lab members are:

Name Role Thesis or main project
Alvaro Martinez Fernandez Master student Thesis: GeneNet VR: Large biological networks in virtual teality using inexpensive hardware
Jo Inge Arnes PhD student NOWAC
Tengel Ekrem Skar Master student, 2019, CS, UiT Thesis: Scalable exploration of population-scale drug consumption data. Source code
Mayeul Marcadella Technical staff ELIXIR
Aleksandr Agafonov Technical staff ELIXIR
Dr. Bjørn Fjukstad PhD student, 2018, CS, UiT Thesis: Toward Reproducible Analysis and Exploration of High-Throughput Biological Datasets. Source code for Kvik and walrus
Tim Alexander Teige Master student, 2018, CS, UiT Thesis: Auto scaling framework, simulator, and algorithms for the META-pipe backend and Source code. Source code
Nina Angelvik Master student, 2018, CS, UiT Thesis: Data management platform for citizen science education projects. Source code: Backend and Frontend
Mike Voets Master student, 2018, CS, UiT Thesis: Deep Learning: From Data Extraction to Large-Scale Analysis. Source code: replication study and DICOM anonymizer
Inge Alexander Raknes Technical staff ELIXIR
Johan Ravn Master student, 2017, CS, UiT Thesis: Detection of Wheezes and Breathing Phases using Deep Convolutional Neural Networks
Dr. Giacomo Tartari Technical staff ELIXIR
Frode Opdahl Master student, 2016, CS, UiT Project: Virtual reality
Jarl Fagerli Master student, 2015, CS, UiT Thesis: COMBUST I/O. Abstractions facilitating parallel execution of programs implementing common I/O patterns in a pipelined fashion as workflows in Spark. Source code
Kenneth Knudsen Master student, 2015, CS, UIT Thesis: Freia: Exploring Biological Pathways Using Unity3D. Source Code. Demo)
Ove Kåven Master student, 2015, CS, UiT Thesis: Multiparadigm Optimizing Retargetable Transdisciplinary Abstraction Language. Source code
Ida Jaklin Johansen Technical staff ELIXIR
Martin Ernstsen Master student, 2013, CS, UiT Thesis: Mario - A system for iterative and interactive processing of biological data. Source code
Terje André Johansen Master student, 2011, CS, UiT Thesis: A scalable, interactive widget library for visualizing biological data