ArticleBioMed research international2016
An Entropy-Based Position Projection Algorithm for Motif Discovery.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026Review
- Revisiting motif finding: do bi-objective metaheuristics surpass single-objective metaheuristics?BMC bioinformatics · 2025Article
- Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks.International journal of molecular sciences · 2024Article
- A survey on deep learning in DNA/RNA motif mining.Briefings in bioinformatics · 2021Article
- Review of Different Sequence Motif Finding Algorithms.Avicenna journal of medical biotechnologyReview
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Authors and funding
3 authors.
Funding
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Abstract
Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms.
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