How to Nurture a Biotech? Sometimes, Like a Teenager
How Inpatient Care Feels Today
Best Practices of Clinical Bioinformatics and Data Science
History, Challenges, and Future Directions of Bioinformatics in...
The Rise of Population Genomics in Target Discovery
Irene Blat, Ph.D., Scientific Director of Translational Genomics, Wuxi NextCODE Genomics
Bioinformatics in Pharma and Biotech Industries: Challenges and...
Brandon W. Higgs, PhD, Head of Translational Bioinformatics, Immunocore & Adjunct Faculty, Johns Hopkins University, Bioinformatics & Biotechnology AAP
Real World Evidence for Precision Medicine
Amrit Ray, Global President, Research, Development and Medical, Pfizer Inc.
Pharmacovigilance: An Opportunity for Technology Investment for the...
Richard Wolf, Executive Director, Pv Operations, Global Clinical Safety & Pharmacovigilance (GCSP), CSL Behring
Thank you for Subscribing to CIO Applications Weekly Brief
Bioinformatics Trends in the Pharmaceutical Industry
By Thorsten Thormann, Vice President, Leo Pharma and David Adrian Ewald ,Head of Bioinformatics, Leo Pharma
As the amount of data and the dimensionality of it generates a level of complexity, that makes it difficult for scientists without some data science skills to interact with this kind of data, there is a general need for solutions that democratize these multi-omics data, and open the possibility for non-data scientist, to browse and interrogate the data. The simplest application is to test hypotheses through the ability to quickly query indication specific molecular in-house data and evaluate the validity or novelty of the described findings. Some proprietary solutions to accommodate these needs exist and have been around for some time.
It is absolutely crucial to ensure proper education of drug discovery scientists to create a solid understanding of the limitations and pitfalls, when making complex data broadly available within an organization
One important point needs to be made here, whichever solution an organization decides on, with data democratization comes a risk of over- or mis-interpretation. An example could be, that a researcher finds a protein enriched in a histological analysis reported ina publication; but the internal data platform does not show the mRNA as being significantly upregulated in the relevant tissue. In this case it might be, that the high-throughput transcriptomics technology presented on the data platform, was simply not sensitive enough to detect this lowly expressed gene, but not being aware of this could lead to unfortunate misinterpretation. So, it is absolutely crucial to ensure proper education of drug discovery scientists to create a solid understanding of the limitations and pitfalls, when making complex data broadly available within an organization.
To conclude, we have seen a great evolvement in terms of the pricing and throughput of omics data generation in the past decades, which has led (and still is) to a vast amount of data in many areas of research. Huge amounts of multi-layered data give us the opportunity to add granularity to our molecular disease understanding, but we have to always remember, as Christine l. Borgman eluted to in her book “Big Data, Little Data, No Data”, that even though “big data” is great to have, we need to be even more focused on the quality of the data.