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Big Data and the Life Sciences

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Presentation on theme: "Big Data and the Life Sciences"— Presentation transcript:

1 Big Data and the Life Sciences
Joshua T. Vogelstein, James Taylor, Elana Fertig, Alexis Battle

2 What is Big Data Science for the Life Sciences?
A field that develops, applies and combines big data systems and machine learning / artificial intelligence with life science domain knowledge to improve health and healthcare.

3

4 Why is it hard? Each stage of the data analysis workflow is broken by big data: Volume - each genome is 1B bytes Velocity - can generate TBs per day Variety - images, questionnaires, text, ‘omics, time-series Veracity - lots of noise

5 Why is it hard? Life sciences introduce a number of additional challenges Wide: sample size is ~10, dimensionality is ~109 Multiscale: molecules, cells, tissues, organs, individuals, populations Interpretable: doctors, scientists, insurers must understand

6 NeuroData Joshua T. Vogelstein

7 NeuroData Neural coding is about brain activity & info. representation
Connectal coding is about brain connectivity & info. storage Connectopathies underly psychiatric & learning disorders Depression is leading cause of disability worldwide NeuroData Vogelstein et al., Current Opinion in Neurobiology (2019)

8 Storing Extended SDSS database Support 3D + multi-spectral data
Now host the largest open source brain image repo in the world Storing Vogelstein et al., Nature Methods, 2018 ( Vogelstein et al., Nature Methods (2018)

9 Wrangling 10 TB raw image volume Bias correct, stitch, register, align
Multi-contrast nonlinear deformation Wrangling Kutten et al., MICCAI (2017)

10 Exploring Often faced with high-dimensional multi-modal data
Desire to understand which variables are nonlinearly associated Juxtapose harmonic analysis with manifold learning Exploring Vogelstein et al., eLife (2019)

11 Modeling Larval Drosophila Mushroom Body Connectome
Network valued data Parsimonious models enable estimation & testing Modeling Athreya et al., JMLR (2018)

12 Predicting Must predict class or magnitude from high-dimensional data
Requires interpretable predictions Juxtapose random matrix theory and decision forests Predicting Tomita et al., arXiv (2018)

13 Acknowledgements

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