ArticleNPJ systems biology and applications2022
NETISCE: a network-based tool for cell fate reprogramming.
Article in NPJ systems biology and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Controllability of the gene regulatory network in zebrafish embryogenesis.Scientific reports · 2025Article
- Cell and tissue reprogramming: Unlocking a new era in medical drug discovery.Pharmacological reviews · 2025Review
- TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics data.Genome research · 2025Article
- Advancing cell therapies with artificial intelligence and synthetic biology.Current opinion in biomedical engineering · 2025Article
- Epigenome editing technologies for discovery and medicine.Nature biotechnology · 2024Review
- Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML.NPJ systems biology and applications · 2024Article
- Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Article
- Reproducibility and FAIR principles: the case of a segment polarity network model.Frontiers in cell and developmental biology · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.
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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.