Why data from preclinical biotech lab experiments make machine learning challenging
Alternatively titled: Why biotech data isn't as straightforward as it seems!
Hello fellow datanistas!
For those of you who are in the biotech world, or are biotech adjacent, or have an interest in solving medicine, this edition is for you!
Have you ever wondered why integrating public datasets with your own research data can be so challenging in the biotech field? In my latest blog post, I explore the common pitfalls and unique hurdles that biotech teams face when working with preclinical data.
The journey often begins with the excitement of discovering a promising public dataset that aligns perfectly with research objectives. However, as teams dive deeper, they encounter fundamental challenges such as reconciling binary and continuous measurements, hunting for invisible variables, and navigating domain shifts. These challenges highlight the inherent complexity of biological systems and the need for a more nuanced approach to data integration. Instead of force-fitting datasets into a single model, I propose a decision support perspective that leverages multiple models and human expertise to make informed decisions.
By embracing a decision support framework, we can better navigate the complexities of biotech data and accelerate discovery. This approach respects the intricacies of biological systems and opens up exciting opportunities for innovation in biotech research.
I invite you to read the full post here. If you find it insightful, please consider sharing it with others who might benefit from this perspective.
Happy reading!
Eric