Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies

PloS One
Simeone MarinoIvo D Dinov

Abstract

The theoretical foundations of Big Data Science are not fully developed, yet. This study proposes a new scalable framework for Big Data representation, high-throughput analytics (variable selection and noise reduction), and model-free inference. Specifically, we explore the core principles of distribution-free and model-agnostic methods for scientific inference based on Big Data sets. Compressive Big Data analytics (CBDA) iteratively generates random (sub)samples from a big and complex dataset. This subsampling with replacement is conducted on the feature and case levels and results in samples that are not necessarily consistent or congruent across iterations. The approach relies on an ensemble predictor where established model-based or model-free inference techniques are iteratively applied to preprocessed and harmonized samples. Repeating the subsampling and prediction steps many times, yields derived likelihoods, probabilities, or parameter estimates, which can be used to assess the algorithm reliability and accuracy of findings via bootstrapping methods, or to extract important features via controlled variable selection. CBDA provides a scalable algorithm for addressing some of the challenges associated with handling comple...Continue Reading

References

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Mar 30, 2010·NeuroImage·Cynthia M StonningtonUNKNOWN Alzheimer Disease Neuroimaging Initiative
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Mar 16, 2016·Scientific Data·Mark D WilkinsonBarend Mons

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Citations

Jul 25, 2019·Journal of Medical Systems·Maria José SousaÁlvaro Rocha

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Methods Mentioned

BETA
genotyping

Software Mentioned

BartMachine
CBDA
LONI pipeline environment for
RandomForest
SuperLearner
Cranium
R markdown script
- SL
R
LONI

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