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Updated: May 16, 2026
03:37
Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
Published on: March 1, 2024
Mining regulatory network connections by ranking transcription factor target genes using time series expression data
Antti Honkela1, Magnus Rattray, Neil D Lawrence
1Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland. antti.honkela@hiit.fi
Methods in Molecular Biology (Clifton, N.J.)
|November 30, 2012
Summary
Inferring gene regulatory networks is hard with limited data. Our new method uses Gaussian processes and ODEs to identify direct transcription factor targets from short time-series expression data, aiding cancer research.
Area of Science:
- Computational Biology
- Systems Biology
- Genomics
Background:
- Gene regulatory network (GRN) inference is complex due to limited data.
- Understanding transcription factor (TF) binding is crucial for GRN analysis.
Purpose of the Study:
- To develop a method for inferring direct TF targets from limited, short time-series expression data.
- To address the challenge of limited data in reverse engineering GRNs.
Main Methods:
- Combines Gaussian process (GP) priors with ordinary differential equation (ODE) models.
- Allows inference from limited, potentially unevenly sampled expression time-series data.
- Implemented as an R/Bioconductor package for accessibility.
Main Results:
- Successfully demonstrated the method's capability in ranking candidate targets.
- Applied to identify potential targets of the p53 tumor suppressor.
Conclusions:
- The presented method offers a robust approach for TF target inference with limited data.
- Facilitates a more focused analysis within the broader challenge of GRN reconstruction.
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