Statistical Genomics using F# with Evelina Gabasova
Can computing cure cancer? That's Evelina Gabasova's goal! Carl and Richard talk to Evelina about her work using machine learning and data analytics to understand the genetics of cancer, its variations and subtypes. Part of her goal is to get to personalized medicine - where your doctor uses your genetic information to determine the ideal treatment, custom-made for you. So how does that involve statistics? Evelina talks about her evolution from computing into informatics and the various tools used to analyze data deeply, rather than widely - including F#!
Evelina Gabasova is a machine learning researcher working in bioinformatics and statistical genomics. She is developing mathematical models which integrate different types of genomic data to distinguish cancer subtypes. She studied computational statistics and machine learning at University College London and currently she is finishing her PhD at Cambridge University in the MRC Biostatistics Unit. Evelina has used many different languages to implement machine learning algorithms, such as Matlab, R or Python. In the end, F# is her favourite and she uses it frequently for data manipulation and exploratory analysis. She writes a blog on F# in data science at evelinag.com. You can also find her on Twitter as @evelgab.
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