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.
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.
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) at UiT is a large inter-disciplinary center with a research focus on antimicrobial resistance. We are responsible for the bioinformatics analyses.
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.
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.
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 and programming guides. Air:bit has been used by 13 high-school classes in Northern Norway.
We are building a dose design framework to identify the best treatment regimens for individual patients. We will develop new machine learning algorithms on high performance computing systems to make the simulations of large antibiotic dosing regimen simulations highly efficient.
Monitoring antimicrobial susceptibility is a core element for the appropriate treatment of bacterial infections and the implementation of infection control measures. The project provides information on local, regional, or national resistance profiles and changes in MIC levels over time by continuous analysis of data from clinical samples from one laboratory or several laboratories.
Ground investigations are the processes of exploring ground conditions for construction and mining operations. This project collaborates with The Coring Company to optimize the ground investigation processes and reduce its potential cost and ground-related risks using Machine Learning approaches.
The lab currently consist of:
|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 Ngoc-Nha Tran||Associate Professor||Co-PI||Homepage and GitHub|
|Helge Fredriksen||Associate Professor||Co-PI||Homepage and GitHub|
|Henrik Løvold||Assistant Professor||Co-PI||Homepage and GitHub|
|Morten Grønnesby||Assistant Professor||NOWAC||Homepage and GitHub|
|Rafael Nozal Cañadas||PhD student||Population studies in the north||GitHub|
|Nikita Shvetsov||PhD student||SFI Visual Intelligence||GitHub, Bitbucket, LinkedIn|
|Pavitra Chauhan||PhD student||Synthetic data||-|
|Anders Sildnes||PhD student||SFI Visual Intelligence||Homepage, Github, Gitlab, LinkedIn|
|Belal Medhat||PhD student||CANS||Homepage, GitHub, Google Scholar, Linkedin, and ResearchGate|
|Jieli Zhu||PhD student||Machine Learning in Ground Investigation||-|
|Theodor Ross||Affiliated PhD student||Machine learning for antimicrobial resistance||-|
|Rigmor Katrine Johansen||Affiliated PhD student||Biomedical ethics||Homepage|
|Mohsen Askar||Affiliated PhD student||Big pharmacoepidemiology||Homepage|
|Masoud Tafavvoghi||Affiliated PhD student||Visual intelligence||-|
|Andrew Daniel Delos Mashchak||Affiliated PhD student||NOWAC||-|
|Solveig Flatebø||Affilitated PhD student||Robots and small children||-|
|Per Niklas Waaler||Scientific staff||TRUSTING||-|
|Bjørn-Richard Pedersen||Affiliated scientific staff||RHD||GitHub|
|Maisha Islam||Affiliated scientific staff||RHD||-|
|A-Young Jang||Master student||Monitoring antimicrobial susceptibility/CANS||-|
|Adam Mawassi||Master student||Large antibiotic dosing regimen simulations/CANS||-|
|Anders Søreide||Master student||-||-|
|Binod Baniya||Master student||with Medsensio||-|
|Dominik Thamm||Master student||with EagleAI||-|
|Muhammad Nauman Alo||Master student||with iTromsø||-|
|Mohammad Zahirul Islam||Master student||-||-|
|Thomas Eide||Master student||Didactics of Computer Science||-|
|Zulfiqar Ali||Master student||Norwegian medical texts||-|
|Thea Ueland||Affiliated Master student||Capia||-|
Former lab members are:
|Name||Role||Thesis or main project|
|Ragnhild Abel Grape||Intern||AI4Europe|
|Marius J. Ingebrigtsen||Intern||AI4Europe|
|Elias Estefano Gutierrez Riise||Master student, 2023||Thesis: Improving automated underwater ship hull inspection through incremental learning & uncertainty quantiﬁcation in deep learning models|
|Erling Devold||Master student, 2023||Thesis: Through Space and Time|
|Simen Lund Kirkvik||Master student||Thesis: Interactive visualizations of unstructured oceanographic data|
|Mirza Aneeq Hassan Baig||Master student||Thesis: Tram-tastic Cloud Computing|
|Asal Asgari||Master student||Thesis: Clustering of clinical multivariate time-series utilizing recent advances in machine-learning Source code|
|Narae Park||Master student||Thesis: Record linkage of Norwegian historical census data using machine learning. Source code|
|Mariel Ellingsen||Master student||Thesis: First steps towards solving the café problem and source code|
|Markus Tiller||Master student||Thesis: End-to-end Trainable Ship Detection in SAR Images with Single Level Features and source code|
|Michael Lau||Master student||Thesis: Management of large geospatial datasets.|
|Christina Rolandsen||Scientific staff||Population studies in the north|
|Dominic Riley||Adjunct advisor||Industry mentor from The Grow Room|
|Jonas Juselius||Adjunct associate professor||Industry mentor from Serit IT-partner|
|Sidra Tahi||Master student||Thesis: Can a code snippet portal contribute to greater learning outcomes in other fields of science and technology?|
|Anton Garri Fagerbakk||Master student||Thesis: Keeping Up with the Market: Extracting competencies from Norwegian job listings|
|Wilhelm Vold||Intern||HeartOn app|
|Alvaro Martinez Fernandez||Master student||Thesis: GeneNet VR: Large biological networks in virtual teality using inexpensive hardware and source code|
|Jo Inge Arnes||PhD student||NOWAC|
|Tengel Ekrem Skar||Master student, 2019, CS, UiT||Thesis: Scalable exploration of population-scale drug consumption data and 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|