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Identifying biomarkers for diagnostic classification and prognostic assessment is increasingly becoming commonplace. This drive to harness an ever-increasing volume and variety of biomarker data has now become critical to help identify patients that could benefit most from emerging therapies. However, leveraging biomarker-driven insights hinges on mining appropriate datasets both for biomarker hypothesis generation and hypothesis testing. This can be especially cumbersome if the necessary bioinformatics skills to extract the best information out of datasets is missing. Simplifying routine data searching, access and analysis also helps your bioinformaticians to focus on answering more complex data queries and analysis.

In this webinar, the presenters focus on how scientists with minimal bioinformatics skills are able to easily mine large datasets to not only extract the most relevant biomarker information from published abstracts but also to test that hypothesis on their biomarker datasets to assess the impact of biomarker(s) on patient populations.

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