Translating disease research into personalized medicine is the promise of next generation sequencing. However, it comes with a host of problems, including overwhelming amounts of data and a huge challenge in translating that data back to implicated biology. Successful NGS data analysis clearly requires a biological analysis component – the ability to get translate large amounts of data into better insights into disease phenotypes that can yield clinically actionable hypotheses.
This whitepaper will explore this approach in more detail, using a prostate adenocarcinoma dataset as an example, with a goal of using an in silico approach to explore patient-specific prostate cancer mechanisms and possible clinical biomarkers. You’ll see how this approach can help you:
- Pinpoint specific pathways and processes activated in growing cancer cells within tumors
- More easily identify affected processes
- Determine treatment options on a patient-specific basis
- Identify potential biomarkers in order to improve patient treatment and prognosis
- Generate plausible in silico hypotheses related to individual patient’s mechanisms of disease
- Use NGS data both for clinical insights as well as clinical research
You can download the free white paper by clicking here.