The Project

Major Aims of the BioGeMT Project

 

  1. To conduct analyses and offer BioIT services to UM research groups, external collaborators, and industry
  2. BioIT advice and support for research work and training for grant and publication writing
  3. Consolidation of HTS data storage at UM
  4. Facilitating data sharing and promoting international collaborations
  5. Compiling an ethics framework for HTS in a Multi-Omic era
  6. Involvement in bioinformatics teaching and supervision at all levels and short training programmes
  7. Opportunities for industry and UM research integration through meetings and collaborations
  8. Integration of Machine Learning with biological data analysis for multi-omics

 

The University of Malta has made a commitment to support the ERA Chair by granting access to research facilities, providing supervision to researchers, and enabling the Chair to apply for funding. In addition, the University will create a permanent position for a Team Leader to ensure the longevity of the Bioinformatics Team beyond the lifespan of the ERA Chair.

The Bioinformatics Team (BT) will offer its services to UM research groups and industry, conducting analysis and providing common pipelines and analysis tools. The ERA Chair and BT will also provide bioinformatics advice and support throughout the research process, including training for grant and publication writing.

To ensure the efficient storage and analysis of High-Throughput Sequencing (HTS) data, the UM will consolidate data storage and make it easier to share and compare data. The ERA Chair will work towards promoting data sharing and international collaborations. Additionally, the Chair will compile an ethics framework for HTS in a Multi-Omic era to help speed up the ethics approval process.

The UM is committed to integrating bioinformatics teaching and supervision at all levels, including postgraduate programmes and short training courses, and will explore opportunities for revenue-generating courses. There will also be opportunities for UM research integration with industry, promoting collaborations and intersectoral mobility.

Machine Learning will be integrated with biological data analysis to develop new ways of maximizing findings from large datasets of multi-Omic and clinical data. These efforts will enhance synergies with other areas of research at UM.

 


https://www.um.edu.mt/projects/biogemt/theproject/