This project, Biomarkers in clinical diagnosis, is one out of three subprojects within BioMine - Data-mining for biomarker discovery, selection, and validation. We will investigate biomarkers in the context of clinical diagnostics by integration of high-throughput methods with advanced data analytic methods to provide critical information for clinical management.
Of specific interest are biomarkers that enabling fast and precise sepsis diagnoses to initiate effective, targeted antibiotic management. The project is performed in close co-production between University of Skövde, University of Gothenburg, TATAA Biocenter AB, Unilabs AB, 1928Diagnostics AB, Skaraborg Hospitals, Olink Proteomics AB, and SciLifeLab.
There is currently a huge interest and expectations around the promise of combination of big data analysis and biomarker discovery. However, there are still many challenges that need to be addressed and especially novel algorithms suitable for combined large-scale analysis of various types of biological data e.g., proteins, nucleic acids, small molecules are currently lacking. 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.
Research question addressed in three sub-projects
The research question addressed herein is how large-scale biomolecular data can be mined to enable discovery, selection, and validation of multilevel biomarkers in life science. The research question will be investigated in three sub-projects covering biomarker discovery and validation in toxicological testing, disease modeling, and clinical sepsis diagnosis with focus is on the generation and analysis of large-scale data.
A substantial effort will be dedicated towards the development of novel approaches and algorithms suitable for analysis of different types of data derived from different sources. The expected outputs from the project are novel multi-marker panels of predictive biomarkers and innovative algorithms that can be exploited for complex discovery of biomarkers from multiple data sources.