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    University of Skövde, link to startpage

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      University of Skövde, link to startpage

      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

      2024

      Sandra Tejedor, Maria Wågberg, Cláudia Correia, Karin Åvall, Mikko Hölttä, Leif Hultin, Michael Lerche, Nigel Davies, Nils Bergenhem, Arjan Snijder, Tom Marlow, Pierre Dönnes, Regina Fritsche-Danielson, Jane Synnergren, Karin Jennbacken et al.

      2023

      Susanna Larsson, Sandra Holmgren, Lachmi Jenndahl, Benjamin Ulfenborg, Raimund Strehl, Jane Synnergren, Nidal Ghosheh

      ACS Omega

      2023. Article. https://doi.org/10.1021/acsomega.3c07098

      Katarzyna Marzec-Schmidt, Nidal Ghosheh, Sören Richard Stahlschmidt, Barbara Küppers-Munther, Jane Synnergren, Benjamin Ulfenborg

      Stem Cells

      2023. Article. https://doi.org/10.1093/stmcls/sxad049 Open Access

      Muhammad Nawaz, Sepideh Heydarkhan-Hagvall, Benyapa Tangruksa, Hernán González-King Garibotti, Yujia Jing, Marco Maugeri, Franziska Kohl, Leif Hultin, Azadeh Reyahi, Alessandro Camponeschi, Bengt Kull, Jonas Christoffersson, Ola Grimsholm, Karin Jennbacken, Martina Sundqvist et al.

      Advanced Science

      2023. Article. https://doi.org/10.1002/advs.202206187 Open Access

      Mikael Sandstedt, Kristina Vukusic, Markus Johansson, Marianne Jonsson, Rasmus Magnusson, Lillemor Mattsson Hultén, Göran Dellgren, Anders Jeppsson, Anders Lindahl, Jane Synnergren, Joakim Sandstedt

      American Journal of Physiology. Heart and Circulatory Physiology

      2023. Article. https://doi.org/10.1152/ajpheart.00438.2023

      Jacob Sjölin, Marianne Jonsson, Charlotta Orback, Anders Oldfors, Anders Jeppsson, Jane Synnergren, Victoria Rotter Sopasakis, Kristina Vukusic

      Stem Cells and Development

      2023. Article. https://doi.org/10.1089/scd.2022.0253 Open Access

      Sören Richard Stahlschmidt, Benjamin Ulfenborg, Jane Synnergren

      Bioinformatics and Biomedical Engineering: 10th International Work-Conference, IWBBIO 2023, Meloneras, Gran Canaria, Spain, July 12–14, 2023, Proceedings, Part I

      2023. Conference paper. https://doi.org/10.1007/978-3-031-34953-9_8

      Klas Österberg, Yalda Bogestål, Lachmi Jenndahl, Tobias Gustafsson-Hedberg, Jane Synnergren, Gustav Holmgren, Eva Bom, Sarunas Petronis, Annika Krona, Jonna S. Eriksson, Jennifer Rosendahl, Veronica Crisostomo, Francisco M. Sanchez-Margallo, Claudia Baez-Diaz, Raimund Strehlc et al.

      Biomaterials Science

      2023. Article. https://doi.org/10.1039/d2bm02011d Open Access

      2022

      Markus Johansson, Benyapa Tangruksa, Sepideh Heydarkhan-Hagvall, Anders Jeppsson, Peter Sartipy, Jane Synnergren

      Markus Johansson, Benjamin Ulfenborg, Christian X. Andersson, Sepideh Heydarkhan-Hagvall, Anders Jeppsson, Peter Sartipy, Jane Synnergren

      Ongoing projects

      Digitalized 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 Biology

      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 Biology

      Deep 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 Biology

      Finished projects

      Biomarkers 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 Biology

      Data-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 Biology

      Biomarkers 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 Biology

      Transplant 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 Biology

      CardioTrophy - 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 Biology

      AlgorOmics - 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 Biology

      Bioinformatics - 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 Biology

      BISON: 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 Informatics

      Human 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 Biology

      Aberrant 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
      Published: 1/9/2020
      Edited: 1/9/2020
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