ArticleNature genetics2016
A predictive computational framework for direct reprogramming between human cell types.
Article in Nature genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 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
149 citing papers in PubMed.
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Article
- Protocol for identifying cellular reprogramming minimal networks using combinatorial transcription factor screening.STAR protocols · 2026Article
- Prediction of parsimonious and temporally sensitive sets of cell fate engineering transcription factors with IMCell.bioRxiv : the preprint server for biology · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- Combinatorial transcription factor interactions drive modular gene regulatory networks.bioRxiv : the preprint server for biology · 2026Article
- Redefining cellular reprogramming with advanced genomic technologies.Nature reviews. Genetics · 2026Review
- Navigating the landscape of direct cellular reprogramming with DiReG.NPJ systems biology and applications · 2026Article
- CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- In Vivo Targeted Reprogramming of Cardiac Fibroblasts for Heart Regeneration: Advances and Therapeutic Potential.Bioengineering (Basel, Switzerland) · 2025Review
- DualNetM: an adaptive dual network framework for inferring functional-oriented markers.BMC biology · 2025Article
- Refate identifies chemical compounds to target trans-regulatory networks for cellular conversion.bioRxiv : the preprint server for biology · 2025Article
- TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics data.Genome research · 2025Article
- Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review.Computational and structural biotechnology journal · 2024Review
- OneSC: a computational platform for recapitulating cell state transitions.Bioinformatics (Oxford, England) · 2024Article
- EnhancerNet: a predictive model of cell identity dynamics through enhancer selection.Development (Cambridge, England) · 2024Article
- Machine Learning-Directed Conversion of Glioblastoma Cells to Dendritic Cell-Like Antigen-Presenting Cells as Cancer Immunotherapy.Cancer immunology research · 2024Article
- Direct Cardiac Reprogramming in the Age of Computational Biology.Journal of cardiovascular development and disease · 2024Review
- Epigenome editing technologies for discovery and medicine.Nature biotechnology · 2024Review
89 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine. The identification of key transcription factors for reprogramming is currently limited by the cost of exhaustive experimental testing of plausible sets of factors, an approach that is inefficient and unscalable. Here we present a predictive system (Mogrify) that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce cell conversion. We have applied Mogrify to 173 human cell types and 134 tissues, defining an atlas of cellular reprogramming. Mogrify correctly predicts the transcription factors used in known transdifferentiations. Furthermore, we validated two new transdifferentiations predicted by Mogrify. We provide a practical and efficient mechanism for systematically implementing novel cell conversions, facilitating the generalization of reprogramming of human cells. Predictions are made available to help rapidly further the field of cell conversion.
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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.