Search results

    Search results

    Show all results for ""
    Can not find any results or suggestions for "."

    Search tips

    • Make sure there are no spelling errors
    • Try different search terms or synonyms
    • Narrow your search for more hits

    How can we help?

    Contact Us

    Find Employees

    University of Skövde, link to startpage

    Search results

      Search results

      Show all results for ""
      Can not find any results or suggestions for "."

      Search tips

      • Make sure there are no spelling errors
      • Try different search terms or synonyms
      • Narrow your search for more hits

      How can we help?

      Contact Us

      Find Employees

      University of Skövde, link to startpage

      Deep Learning for ATMP

      Research Group Translational Bioinformatics
      Resarch Environment Systems Biology

      Deep Learning for ATMP

      Research Group Translational Bioinformatics
      Resarch Environment Systems Biology

      Quick Facts

      Full project name

      Transfer Learning across technologies for ATMP

      Duration

      October 2022 – September 2024

      Funding and collaboration

      VINNOVA, RISE, MultiD Analyses, Takara Bio Europe, TATAA Biocenter

      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.

      Why is the project unique?

      The innovation and uniqueness of this project resides in two main aspects: interpretation of neural networks and transfer learning across technologies. Firstly, the interpretation of the representations learned in the initial NN classifier developed using single-cell RNA-seq data will allow the identification of transcriptomic signatures associated with cell quality. Secondly, the knowledge contained in the NN classifier developed using sequencing-based data will be transferred to a NN classifier that will use qPCR-based data as input.

      Advantage with approach

      By developing a NN classifier for quality control of cells based on qPCR data, we aim to develop an affordable method that can be implemented in the quality control process at our industrial partners, resulting in an increased accuracy for prediction of cell quality and the significant associated cost reduction. Likewise, the panel of biomarkers associated with cell quality identified by the DL-based model will serve as the basis for the development of sophisticated qPCR-based assays.

      Integration of method

      Finally, this method will also be integrated as package in dedicated advanced qPCR data analysis software for its commercialization.

      Industrial partners

      The project is coordinated by the University of Skövde and executed in close collaboration with our industrial partners Takara Bio Europe Swedish filial, RISE, TATAA Biocenter AB, and MultiD Analyses AB.

      Project Leader

      Professor of BioInformatics

      Participating Researchers

      Amin forootan
      MultiD Analyses
      Catharina Brandsten
      Takara Bio
      Joakim Håkansson
      RISE
      Mikael Kubista
      Tataa Biocenter

      Funding and collaboration

      Published: 3/31/2023
      Edited: 3/31/2023
      Responsible: webmaster@his.se