ArticleNature machine intelligence2020
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.
Article in Nature machine intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Immunological maladaptation preceding spontaneous preterm birth in human pregnancies.Nature communications · 2026Article
- A lesson from the past and the future of artificial intelligence in medical decision-making.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Pretraining and the lasso.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- Systemic immune dysregulation in hypertensive disorders of pregnancy persists years after delivery.Frontiers in immunology · 2026Article
- Systemic immune dysregulation in hypertensive disorders of pregnancy persists years after delivery.bioRxiv : the preprint server for biology · 2025Article
- Design Principles of Nanosensors for Multiplex Detection of Contaminants in Food.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
- A machine learning approach to leveraging electronic health records for enhanced omics analysis.Nature machine intelligence · 2025Article
- Peripheral Single-Cell Immune Characteristics Contribute to the Diagnosis of Alzheimer's Disease and Dementia With Lewy Bodies.CNS neuroscience & therapeutics · 2025Article
- Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.BMC bioinformatics · 2024Article
- Unlocking human immune system complexity through AI.Nature methods · 2024Article
- Artificial Intelligence in Perioperative Care: Opportunities and Challenges.Anesthesiology · 2024Article
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies.The Lancet. Respiratory medicine · 2024Review
- 3D molecular generative framework for interaction-guided drug design.Nature communications · 2024Article
- A multi-omics systems vaccinology resource to develop and test computational models of immunity.Cell reports methods · 2024Article
- DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology.GigaScience · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- The benefits and pitfalls of machine learning for biomarker discovery.Cell and tissue research · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
31 authors.
Funding
Abstract
The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.
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.