ArticlePLoS computational biology2023
The effect of non-linear signal in classification problems using gene expression.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- UPR-CEBPB-MIF Signaling Links to Macrophage Polarization and an Immunosuppressive Tumor Microenvironment in Clear Cell Renal Cell Carcinoma.Annals of surgical oncology · 2026Article
- Present and future opportunities for artificial intelligence applications in dairy cattle reproduction.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- HABiC: an algorithm based on the exact computation of the Kantorovich-Rubinstein optimizer for binary classification in transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
- Application of single-cell deep learning in elucidating the mapping relationship between visceral and body surface inflammatory patterns.Computational and structural biotechnology journal · 2025Article
- Best holdout assessment is sufficient for cancer transcriptomic model selection.Patterns (New York, N.Y.) · 2024Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.