ArticleCell systems2024
Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.
Article in Cell systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Beyond suppression: the paradox of JAK inhibitors as amplifiers of cancer immunotherapy.Nature reviews. Immunology · 2026Review
- Ligand discrimination in immune cells: Signal processing insights into immune dysfunction in ER+ breast cancer.PLoS computational biology · 2025Article
- Machine Learning Reassessment of Serum Immune Factors Shows No Unique Immune Profiles Linked to Disease Outcomes in SARS-CoV-2-infected Patients at Hospital Admittance.European journal of immunology · 2025Article
- Lymphocytes and monocytes undergo swift suppression of IL-10R, IL-6R, and IL-2Rβγ signaling under high concentrations of different cytokines.bioRxiv : the preprint server for biology · 2025Article
- Modeling the response to interleukin-21 to inform natural killer cell immunotherapy.Immunology and cell biology · 2025Article
- Multi-physiology modeling of the immune system in the era of precision immunotherapy.Frontiers in immunology · 2025Review
- Linking signal input, cell state, and spatial context to inflammatory responses.Current opinion in immunology · 2024Review
- Using In silico models to predict lymphocyte activation and development in a data rich era.Immunoinformatics (Amsterdam, Netherlands) · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.
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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.