In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.

In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply-curated disease datasets from QIAGEN OmicSoft Lands.

Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:
• Which genes aren't expressed in normal tissue, yet are expressed in diseases of interest, based on experimental evidence?
• Which of these proteins are cell surface proteins, with evidence for extracellular localization?
• How are these proteins related directly or indirectly to disease pathways, and can these be connected to known drug targets?
• Can we identify correlated biomarkers, mutation targets, clinical factors or other means of cohort selection?