Integrating external and internal ‘omics data is a key element of hypothesis generation, target identification, and biomarker prioritization when it comes to drug discovery. It is never as straightforward as it seems and usually costs more and takes more time than anticipated, often with poor results. In this talk, Joseph Pearson, PhD, from QIAGEN Digital Insights will discuss approaches and resources to overcome these obstacles and improve outcomes.
1. Identify where automated curation approaches can leave you short.
2. Discuss why data scientists spend most of their time not doing data science.
3. Identify where bioinformaticians and data scientist spend most of their time.
4. Explain what is required of high-quality ‘omics data.
5. Analyze the example of high-quality TCGA data.
6. Explain how to extend these practices to internal data structures.