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Scalable Learning for Reproducibility in High-Dimensional Biomedical Signal Processing: A Robust Data Science Framework

CORDIS · observation · Publication date unknown

Signal Processing: A Robust Data Science Framework Data science has quickly expanded the boundaries of signal processing and statistical learning beyond their accustomed domains. Powerful and complex machine learning architectures have evolved to distinguish relevant information from randomness, artifacts and irrelevant data. However, existing learning frameworks lack computationally scalable, tractable, and robust methods for high-dimensional data. Consequently, discoveries, for example, in genomic data can be the result of coincidental findings that happen to reach statistical significance. As long as groundbreaking advances in biotechnology are not accompanied by appropriate learning frameworks, valuable efforts are spent on researching false positives. ScReeningData develops a coherent fast and scalable learning framework that jointly addresses the fundamental challenges of drastically reducing computational complexity, providing statistical and robustness guarantees, and quantifying reproducibility in large-scale and high-dimensional settings. An unprecedented approach is develo

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR