An Integrated Approach to Biomarker Discovery
One of the main challenges of biomarker discovery is the unbiased evaluation and rational selection of most promising biomarkers suitable for independent validation. The absence of a robust evaluation procedure can result in selecting too many biomarkers (thus creating multiplicity problems) with biased and often too optimistic estimates of performance and with limited clinical utility.
Almac has developed an integrated approach to biomarker model development, evaluation and selection and applied it to real life projects. This approach is implemented in classification workflow software. This allows for parallel development of multiple biomarkers models and simultaneous generation of the data used in the model evaluation procedure we are going to describe in this webinar. An important feature of this procedure is the use of both statistical and functional characteristics of the models evaluated. While traditional approaches to biomarker model generation commonly focus on purely statistical properties, in this webinar we demonstrate how functional analysis can be used as both an essential component of biomarker discovery as well as integral part of the biomarker selection process.
View this webinar and learn:
- How some of the major challenges of biomarker discovery can be addressed in an integrated manner.
- How to discover and select the biomarker models most suitable for independent validation.
- The importance of functional analysis as an essential component of biomarker discovery.
Presenting this wbebinar:
- Max Bylesjo, Ph.D., Bioinformatics Team Leader, Almac
- Nicolas Goffard, Ph.D, Bioinformatics Scientist, Almac
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