Big Data and the Life Sciences

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Presentation transcript:

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

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.

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

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

NeuroData Joshua T. Vogelstein http://neurodata.io

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) http://neurodata.io

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 (https://neurodata.io/ndcloud/) Vogelstein et al., Nature Methods (2018) http://neurodata.io/ndcloud/

Wrangling 10 TB raw image volume Bias correct, stitch, register, align Multi-contrast nonlinear deformation Wrangling Kutten et al., MICCAI (2017) http://neurodata.io/ndreg/

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) http://neurodata.io/mgc/

Modeling Larval Drosophila Mushroom Body Connectome Network valued data Parsimonious models enable estimation & testing Modeling Athreya et al., JMLR (2018) http://neurodata.io/graspy/

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) http://neurodata.io/rerf/

Acknowledgements