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Use high-through put data analysis to predict candidates for pharmaceutical drug target
For any bio-pharma company, selecting a promising target for drug discovery is a critical step in a very costly drug research & development process. Companies either have a special group of scientists working on target selection full-time or dedicate time to it early in the process. In this Build Project, you will wear the hat of a Data Scientist and perform target selection through the analysis of next generation sequencing data. Under the supervision of an experienced Build Fellow, you will become familiar with high-throughput database website, data download and analysis, and propose 2-3 targets for a selected disease. All this will happen in an environment that simulates the operation of a bioinformatician team.
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Yanling is a Build Fellow specializing in biotechnologies at Open Avenues, where she works with students leading projects in molecular biology.
Yanling is a scientist at FireCyte Therapeutics where she works on exploring novel treatment strategies for neurodegenerative diseases. Yanling has over 10 years of research experience in molecular biology, focusing on obesity, fatty liver disease and immunology. Yanling holders a PhD degree in Biology, and she is applying all her expertise in the neuron-inflammatory disease.
In her own time, she likes playing badminton, hiking.