Jane Synnergren
Professor of BioInformatics
School of Bioscience
Room G2262
I am Professor in bioinformatics at the University of Skövde, Sweden, and Head of the Translational Bioinformatics research group consisting of a group of 19 researchers with a dedicated interest in bioinformatics. I have a background in Data Science and received my MSc in Computer Sciences from the University of Skövde in 2003. During my graduate studies I developed my expertise in bioinformatics and large-scale omics data analysis and received my PhD from the University of Gothenburg in 2010.
My research is focused on analysis methods for identification of biomarkers and integration of multiple types of omics data. I am PI for several research projects specialized in stem cell differentiation, toxicity testing and disease modeling using stem cell based in vitro systems. I am also leading projects particularly focused on advanced analysis of big data, biomarker identification, and data integration with particular interest in machine-learning methods.
Working in close collaboration with the stem cell industry and the pharmaceutical industry we perform global expression studies including transcriptomics, proteomics, epigenomics, and metabolomics of human pluripotent stem cells during their differentiation towards functional cell types. The results from these studies have revealed mechanistic highlights during the early phases of stem cell differentiation and identified biomarkers with potential importance for applications in toxicity testing, drug development, and disease modeling.
https://www.researchgate.net/profile/Jane_Synnergren
Research
Publications
Projects
2025
Cardiovascular Research
2025. Article.
https://doi.org/10.1093/cvr/cvaf037
2024
Cells
2024. Article.
https://doi.org/10.3390/cells13161339
Patterns
2024. Article.
https://doi.org/10.1016/j.patter.2024.101093
Journal of Extracellular Vesicles
2024. Article.
https://doi.org/10.1002/jev2.12445
ACS Omega
2024. Article.
https://doi.org/10.1021/acsomega.3c07098
Molecular Aspects of Medicine
2024. Article, review.
https://doi.org/10.1016/j.mam.2024.101302
2024 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
2024. Conference paper. https://doi.org/10.1109/CIBCB58642.2024.10702166
Cells
2024. Article.
https://doi.org/10.3390/cells13050414
2023
Stem Cells
2023. Article.
https://doi.org/10.1093/stmcls/sxad049
Advanced Science
2023. Article.
https://doi.org/10.1002/advs.202206187
2022
2021
2020
2019
2018
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2016
2015
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2006
Ongoing projects
AI-driven precision medicine provides tailored treatments
The AI-Driven Precision Medicine (AID-PM) initiative aims to position Sweden nationally and internationally as a leading actor in precision medicine. Through close collaboration between academia, industry and healthcare, new AI-based solutions will be developed to improve diagnostics, treatments and drug development.
October 2025 - September 2033 DHEAR,Systems BiologyDigitalized AI tool for safe ATMPs
Advanced Therapy Medicinal Products (ATMPs) is a rapidly emerging field in medicine with great potential, where there is a growing need for robust and accurate methods to characterize and classify processes to assess safety aspects. This project gathers stakeholders from academia, research institutes and industry from various disciplines to develop AI-based methods for safety assessment of ATMPs.
November 2023 - October 2026 Systems BiologyFinished projects
BIO-AID - Biomedical AI-driven data analytics
Artificial intelligence (AI) is an important driving force that is rapidly transforming health care and pharmaceutical industries in several ways. The vast amount of biomedical data available today poses unique opportunities to develop a repertoire of AI-based models. Although the results from studies using AI for solving biomedical problems are encouraging, there are numerous scientific challenges associated with AI for life science applications that need to be addressed.
October 2020 - September 2024 Systems BiologyDeep Learning for ATMP
In this project we develop and implement an innovative Deep-Learning (DL) based method for quality assessment for industrial use. By using human embryonic stem cells as a model system, we develop in different steps a neural network (NN) classifier for a stepwise prediction of the cell state (quality) of these cells using quantitative PCR (qPCR) data.
October 2022 - September 2024 Systems BiologyBiomarkers for toxicity testing
This project is one of the three subprojects within the synergy project BioMine - Data-mining for biomarker discovery, selection, and validation. In this subproject we investigate how large-scale biomolecular data can be used to identify specific biomarkers for toxicity testing. The project is performed in close collaboration between the University of Skövde, AstraZeneca Gothenburg and Takara Bio Europe.
October 2017 - May 2022 Systems BiologyData-mining for Biomarker Discovery, Selection, and Validation
In the BioMine project (Data-mining for Biomarker Discovery, Selection, and Validation), studies are performed on how large-scale biomolecular data can be mined to enable discovery and validation of multilevel biomarkers in Life Science.
October 2017 - May 2022 Systems BiologyBiomarkers for disease modelling
This project is one of the three subprojects within the synergy project BioMine - Data-mining for biomarker discovery, selection, and validation. In this subproject we investigate how large-scale biomolecular data can be used to identify specific biomarkers for disease modelling
October 2017 - May 2022 Systems BiologyTransplant Tissue Engineering
The Transplant Tissue Engineering (TransTissuE) is a collaboration project between the University of Skövde, VERIGRAFT and XVIVO. We develop methods and strategies for optimization of the production process of personalized tissue-engineered vascular transplants.
April 2019 - September 2021 Systems BiologyCardioTrophy - for reduced morbidity and mortality in cardiovascular diseases
The advancement in stem cell research over the last decade has now made it possible to generate large quantities of human specialized cells for in vitro applications. Specifically, in the drug discovery and development process this has important implications. The project involves studies of the genetic and molecular basis of hypertrophy and aims to develop new knowledge that can contribute to the development of novel therapies and treatments that can reduce cardiovascular morbidity and mortality.
March 2017 - February 2021 Systems BiologyAlgorOmics - developing new algorithms for biomarker identification
Within AlgorOmics we develop and implement algorithms for integration, visualization, and analysis of large-scale omics data, with applications in stem cell differentiation and drug development.
April 2017 - March 2020 Systems BiologyBioinformatics - Biomedical Big Data
The aim of this project is to contribute with improved methods for analysis, integration, and visualization of biomedical big data. Recent years it has been a massive digitalization of all types of data and information in the society and the majority of all information in the world is nowadays anticipated to be digitalized. This encompasses enormous possibilities for generation of new knowledge but also puts demands on competence and tools for analysis and interpretation of big and complex data, e.g. to identify and extract patterns and information from different data sources. To meet these increasing demands of large-scale data analysis more competence, better and faster algorithms, and powerful computers are needed for execution these algorithms.
October 2015 - September 2019 Systems BiologyBISON: Better decisions through Big Data
Big data has gained much interesting in recent years due to the rapid expansion of the massive amount of data that is available for solving different types of tasks within many different application domains. However, today's big data is still on a fairly low level of abstraction when it comes to complex decision support tasks, subject to e.g. high dimensionality and significant portions of uncertainty regarding which patterns to look for in the data.
October 2015 - September 2019 InformaticsHuman stem cell based in vitro model of the blood brain barrier
The pharmaceutical industry has an urgent need for in vitro model systems with high human relevance that can be used for toxicity testing, drug development, and disease modelling. The project aims at developing a human in vitro model based on human pluripotent stem cells that can mimic important aspects of the blood-brain-barrier.
October 2015 - September 2018 Systems BiologyAberrant DNA methylation patterns in hepatocytes derived from human pluripotent stem cells
Human pluripotent stem cells (hPSC) are characterized by their unique capacities of self-renewal and differentiation, and, as such, they provide an excellent human cell source in basic research, drug discovery and regenerative medicine.
July 2013 - June 2016 Systems Biology