ArticleCell systems2019
MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease.
Article in Cell systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrative transcriptomics and hypothesis-driven transfer machine learning reveal conserved and species-specific host-parasite dynamics acrossFrontiers in cellular and infection microbiology · 2026Pooled it
- Synthesizing Mechanistic Hypotheses from Single-Cell Omics via Discretized Feature Attribution and Empirical Language Model Grounding.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Transplatformer: translating toxicogenomic profiles between generations of platforms.BMC bioinformatics · 2026Article
- SALVE: prediction of interorgan communication with transcriptome latent space representation.American journal of physiology. Heart and circulatory physiology · 2025Article
- Pulmonary Fibrosis-Focusing on the Future: Aspen Lung Conference 2024 Summary.American journal of respiratory cell and molecular biology · 2025Article
- Unsupervised Domain Adaptation with Raman Spectroscopy for Rapid Autoimmune Disease Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- MOTL: enhancing multi-omics matrix factorization with transfer learning.Genome biology · 2025Article
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets.Scientific reports · 2025Article
- GRACKLE: an interpretable matrix factorization approach for biomedical representation learning.Bioinformatics (Oxford, England) · 2025Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- PLIERv2: bigger, better and faster.bioRxiv : the preprint server for biology · 2025Article
- Can AI reveal the next generation of high-impact bone genomics targets?Bone reports · 2025Review
- Translational approaches to the study of eosinophils in vasculitis.Rheumatology (Oxford, England) · 2025Review
- A Pathway-Level Information ExtractoR (PLIER) framework to gain mechanistic insights into obesity in Down syndrome.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development.Current drug discovery technologies · 2025Review
- Article
- An efficient, not-only-linear correlation coefficient based on clustering.Cell systems · 2024Article
- Probabilistic pathway-based multimodal factor analysis.Bioinformatics (Oxford, England) · 2024Article
16 more citing papers are in PubMed but not listed here.
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7 authors.
Funding
Abstract
Most gene expression datasets generated by individual researchers are too small to fully benefit from unsupervised machine-learning methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. To address this challenge, we utilize transfer learning to extract coordinated expression patterns and use learned patterns to analyze small rare disease datasets. We trained a pathway-level information extractor (PLIER) model on a large public data compendium comprising multiple experiments, tissues, and biological conditions and then transferred the model to small datasets in an approach we call MultiPLIER. Models constructed from the public data compendium included features that aligned well to known biological factors and were more comprehensive than those constructed from individual datasets or conditions. When transferred to rare disease datasets, the models describe biological processes related to disease severity more effectively than models trained only on a given dataset.
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