BioMine: Data-mining for Biomarker Discovery, Selection, and Validation
In the BioMine project, studies are performed on how large-scale biomolecular data can be mined to enable discovery and validation of multilevel biomarkers in Life Science.
Biomarkers are becoming increasingly important in the biomedical field, and there is a substantial and urgent need for identification of novel markers to aid disease diagnosis, monitor disease progression, enable personalized medicine, and detect early therapeutic and adverse responses to drugs. Moreover, biomarkers that are representative for various cellular processes are important in cell biology and can be useful for determination of cell identity, for isolation of specific cell populations as well as for monitoring physiological or pathophysiological processes in vitro.
The interest and expectations of combinations of big data analysis and biomarker discovery are huge, although there are still many challenges that need to be addressed and overcome. Novel algorithms suitable for combined large-scale analysis of various types of biological data are urgently needed. The technological capability to measure multitudes of biomarkers has outpaced the sophistication of the available analytical approaches in interpreting this amount of data. Thus, discovery efforts in biomarker science lag behind those in genomics, where large-scale collaboration and multiple-step replication are now standard operating procedures and where the discovery procedure is well established.
In the BioMine project, studies are performed on how large-scale biomolecular data can be mined to enable discovery and validation of multilevel biomarkers in Life Science. Studies in three different research areas are carried out with focus on the generation and analysis of large-scale data. The sub-projects include toxicological testing, disease modeling, and clinical diagnosis. Substantial efforts are dedicated to develop novel approaches and algorithms suitable for analysis of different types of data and from different sources. The project are performed in close collaboration between University of Skövde and our industry partners AstraZeneca Gothenburg, Takara Bio Europe AB, Unilabs AB, 1928 Diagnostics AB, TATAA Biocenter AB, and Olink Proteomics AB.
Anticipated outputs from the project are novel multi-marker panels of predictive biomarkers, innovative algorithms, and analysis pipelines that can be exploited for complex discovery of biomarkers from multiple data sources. Both the industrial and the academic perspectives are reflected in the project. Activities effectively addressing key challenges and needs from the industry are considered and pure academic scientific approaches lead by the research team at the University secure that state-of-the-art methodologies are developed and disseminated throughout the project period.