ArticleScientific reports2020
OutPredict: multiple datasets can improve prediction of expression and inference of causality.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 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
11 citing papers in PubMed.
- Shining light on Arabidopsis regulatory networks integrating nitrogen use and photosynthesis.The Plant journal : for cell and molecular biology · 2025Article
- Rewiring gene circuitry for plant improvement.Nature genetics · 2024Review
- Nitrogen sensing and regulatory networks: it's about time and space.The Plant cell · 2024Review
- Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications.Frontiers in genetics · 2024Article
- EnsInfer: a simple ensemble approach to network inference outperforms any single method.BMC bioinformatics · 2023Article
- Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Integrative omics approaches for biosynthetic pathway discovery in plants.Natural product reports · 2022Review
- The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues.The Plant journal : for cell and molecular biology · 2022Review
- Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice.Frontiers in plant science · 2022Article
- Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.Annual review of plant biology · 2021Review
- ConnecTF: A platform to integrate transcription factor-gene interactions and validate regulatory networks.Plant physiology · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
The ability to accurately predict the causal relationships from transcription factors to genes would greatly enhance our understanding of transcriptional dynamics. This could lead to applications in which one or more transcription factors could be manipulated to effect a change in genes leading to the enhancement of some desired trait. Here we present a method called OutPredict that constructs a model for each gene based on time series (and other) data and that predicts gene's expression in a previously unseen subsequent time point. The model also infers causal relationships based on the most important transcription factors for each gene model, some of which have been validated from previous physical experiments. The method benefits from known network edges and steady-state data to enhance predictive accuracy. Our results across B. subtilis, Arabidopsis, E.coli, Drosophila and the DREAM4 simulated in silico dataset show improved predictive accuracy ranging from 40% to 60% over other state-of-the-art methods. We find that gene expression models can benefit from the addition of steady-state data to predict expression values of time series. Finally, we validate, based on limited available data, that the influential edges we infer correspond to known relationships significantly more than expected by chance or by state-of-the-art methods.
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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.