Our goal for this analysis was to ask questions such as:
- What is the growth rate of publications focused on specific cancer or biomarker classes?
- How are selected cancer types associated with each other?
- Which molecular entities are “associated” with specific disease types?
- Which combination[s] of molecular entities can provide a unique signature for specific disease types?
- Can the publications dataset predict which signatures are most promising as biomarkers?
In order to answer the questions, we first…
- built a broad database of cancer biomarker publications using PubMed data, and then
- categorized and segmented the database into 36 cancer types and 441 molecular entities.
CLICK HERE to download the PDF report.
NEW: Ask the Biotech Analyst!
Got a question about cancer biomarkers? Dr. Razvi, co-author of this report, is available to answer your questions!
Simply submit your question in the Readers' Comments box below by no later than Wednesday, September 4, and keep your eyes peeled for a special GEN News Highlight where we will present Dr. Razvi's answers!