The effect of non-linear signal in classification problems using gene expression.

Autor: Heil BJ; Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, United States of America., Crawford J; Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, United States of America., Greene CS; Department of Pharmacology, University of Colorado School of Medicine, Colorado, United States of America.; Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Colorado, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Mar 27; Vol. 19 (3), pp. e1010984. Date of Electronic Publication: 2023 Mar 27 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1010984
Abstrakt: Those building predictive models from transcriptomic data are faced with two conflicting perspectives. The first, based on the inherent high dimensionality of biological systems, supposes that complex non-linear models such as neural networks will better match complex biological systems. The second, imagining that complex systems will still be well predicted by simple dividing lines prefers linear models that are easier to interpret. We compare multi-layer neural networks and logistic regression across multiple prediction tasks on GTEx and Recount3 datasets and find evidence in favor of both possibilities. We verified the presence of non-linear signal when predicting tissue and metadata sex labels from expression data by removing the predictive linear signal with Limma, and showed the removal ablated the performance of linear methods but not non-linear ones. However, we also found that the presence of non-linear signal was not necessarily sufficient for neural networks to outperform logistic regression. Our results demonstrate that while multi-layer neural networks may be useful for making predictions from gene expression data, including a linear baseline model is critical because while biological systems are high-dimensional, effective dividing lines for predictive models may not be.
Competing Interests: The authors declare that they have no conflict of interest.
(Copyright: © 2023 Heil et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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