Reference : The need of standardised metadata to encode causal relationships: Towards safer data-...
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/48971
The need of standardised metadata to encode causal relationships: Towards safer data-driven machine learning biological solutions
English
Garcia Santa Cruz, Beatriz mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Vega Moreno, Carlos Gonzalo mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core >]
Hertel, Frank mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > >]
16-Nov-2021
6
Yes
International
Computational Intelligence Methods for Bioinformatics and Biostatistics 2021
from 14-10-2021 to 16-10-2021
[en] confounders ; causality ; metadata ; machine learning ; systems biology
[en] In this paper, we discuss the importance of considering causal relations in the development of machine learning solutions to prevent factors hampering the robustness and generalisation capacity of the models, such as induced biases. This issue often arises when the algorithm decision is affected by confounding factors. In this work, we argue that the integration of causal relationships can identify potential confounders. We call for standardised meta-information practices as a crucial step for proper machine learning solutions development, validation, and data sharing. Such practices include detailing the dataset generation process, aiming for automatic integration of causal relationships.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/48971
10.5281/zenodo.5729350

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