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Big Data and the Life Sciences

Explore the challenges and advancements in utilizing big data and machine learning in life sciences to enhance healthcare outcomes and understanding brain activity and connectivity. Learn about NeuroData research on neural coding and Connectopathies in psychiatric disorders.

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Big Data and the Life Sciences

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  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 knowledgeto improve health and healthcare.

  3. 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

  4. 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

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

  6. 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 Vogelstein et al., Current Opinion in Neurobiology (2019) http://neurodata.io

  7. Storing • Extended SDSS database • Support 3D + multi-spectral data • Now host the largest open source brain image repo in the world Vogelstein et al., Nature Methods, 2018 (https://neurodata.io/ndcloud/) Vogelstein et al., Nature Methods (2018) http://neurodata.io/ndcloud/

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

  9. Exploring • Often faced with high-dimensional multi-modal data • Desire to understand which variables are nonlinearly associated • Juxtapose harmonic analysis with manifold learning Vogelstein et al., eLife (2019) http://neurodata.io/mgc/

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

  11. Predicting • Must predict class or magnitude from high-dimensional data • Requires interpretable predictions • Juxtapose random matrix theory and decision forests Tomita et al., arXiv (2018) http://neurodata.io/rerf/

  12. Acknowledgements

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