ArticleProceedings of the National Academy of Sciences of the United States of America2020
Deep learning of immune cell differentiation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it, 121 citations in OpenAlex.
- Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.Briefings in bioinformatics · 2023Pooled it
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- Cooperativity enables widespread role of low-affinity motifs in chromatin accessibility and increases regulatory potential.Cell genomics · 2026Article
- DeepGeSeq: deep learning library for genomic sequence modeling and analysis.Bioinformatics (Oxford, England) · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Decoding common and rare noncoding variant effects across cellular and developmental contexts.Nature genetics · 2026Article
- Immune-evasive stem cells: engineering tolerance and reprogramming microenvironments for regenerative therapy.Stem cell research & therapy · 2026Review
- Identification of novel DNA sequence motifs that modulate transcription in T cells.BMC genomics · 2026Article
- Refining sequence-to-activity models by increasing model resolution.Bioinformatics advances · 2026Article
- Widespread low-affinity motifs enhance chromatin accessibility and regulatory potential in mESCs.bioRxiv : the preprint server for biology · 2025Article
- Review
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- Programming human cell type-specific gene expression via an atlas of AI-designed enhancers.bioRxiv : the preprint server for biology · 2025Article
- Iterative deep learning design of human enhancers exploits condensed sequence grammar to achieve cell-type specificity.Cell systems · 2025Article
- Refining sequence-to-activity models by increasing model resolution.bioRxiv : the preprint server for biology · 2025Article
- Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization.Scientific reports · 2025Article
- An era of immunological discoveries heralded by molecular biology.Trends in immunology · 2025Article
- Recipes and ingredients for deep learning models of 3D genome folding.Current opinion in genetics & development · 2025Review
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart.bioRxiv : the preprint server for biology · 2025Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.
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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.