| 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| Scientific congresses, symposiums and conference proceedings : Unpublished conference | |||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  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| Vega Moreno, Carlos Gonzalo [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core >] | |
| Hertel, Frank [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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