ArticleGenome biology2023
PAUSE: principled feature attribution for unsupervised gene expression analysis.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
The trial behind it
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
12 citing papers in PubMed.
- Toward next-generation machine learning and deep learning for spatial omics.Briefings in bioinformatics · 2026Review
- scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics.Nature communications · 2025Article
- sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models.bioRxiv : the preprint server for biology · 2025Article
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.Nature methods · 2024Review
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology.Genome biology · 2024Article
- Interpretable deep learning in single-cell omics.Bioinformatics (Oxford, England) · 2024Review
- Reliable interpretability of biology-inspired deep neural networks.NPJ systems biology and applications · 2023Article
- Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection inBiology · 2023Article
- PAUSE: principled feature attribution for unsupervised gene expression analysis.Genome biology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These methods can be separated into two groups: post hoc analyses of black box models through feature attribution methods and approaches to build inherently interpretable models through biologically-constrained architectures. We argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose PAUSE ( https://github.com/suinleelab/PAUSE ), an unsupervised pathway attribution method that identifies major sources of transcriptomic variation when combined with biologically-constrained neural network models.
Indexed as
Identifiers
What OpenQuestion holds
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.