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

Objective

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 developed that builds upon very recent work of the PI. The underlying concept is to repeat randomized controlled experiments that use computer-generated fake variables as negative controls to trigger an early stopping of the learning algorithms, thereby mitigating the so-called curse of dimensionality. In contrast to existing methods, the proposed methods are completely tractable and scalable to ultra-high dimensions. The gains of developing advanced robust learning methods that are computed ultra-fast and with tight guarantees on the targeted rate of false positives are enormous. They lead to new reproducible discoveries that can be made with high statistical power. Due to the fundamental nature and the broad applicability of the proposed learning methods, the impacts of this project extend far beyond the considered biomedical signal processing use-cases, benefitting all scientific domains that analyze high-dimensional data.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2021-STG

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Host institution

TECHNISCHE UNIVERSITAT DARMSTADT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 500 000,00
Address
KAROLINENPLATZ 5
64289 DARMSTADT
Germany

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Region
Hessen Darmstadt Darmstadt, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 500 000,00

Beneficiaries (1)

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