Common pitfalls for applying deep learning to genomics

The genomics field has undergone a big data revolution with the advent of NGS, which has allowed researchers to take molecular measurements such as gene expression at a genomic scale. This technological advancement has led to a greater understanding of cellular and biological processes and has shown promise for treating many uncurable diseases in clinical settings. As the amount and complexity of genomic data increased, researchers started to leverage DL to extract useful biological information and build predictive models. This has led to many DL tools being used for a wide variety of genomic analysis tasks, such as processing raw data, integrating heterogeneous datasets, predictive modeling, and so on. To prevent low model performance when applying DL for genomics data, there are common pitfalls that one should be aware of. Let’s discuss the common pitfalls that you might face when trying to apply DL to genomic tasks and...