ArticleNAR genomics and bioinformatics2023
TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Benchmarking large language models for identifying transcription factor regulatory interactions.Bioinformatics (Oxford, England) · 2026Article
- Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches.Forestry research · 2025Article
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Authors and funding
9 authors.
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Abstract
Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver. The synthetic data generated in a general setting was used to test four TF-TG identification methods, ENET-based methods performed better than Lasso-based methods. Synthetic data generated from two network settings was used to test Huber-Net and MSE-Net, which outperformed all other methods. The TF-TG identification methods were also tested with SND1 and
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Registered trials
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