ArticleNucleic acids research2010
Discovering protein-DNA binding sequence patterns using association rule mining.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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, 56 citations in OpenAlex.
- mtDNA Single-Nucleotide Variants Associated with Type 2 Diabetes.Current issues in molecular biology · 2023Article
- Imbalanced target prediction with pattern discovery on clinical data repositories.BMC medical informatics and decision making · 2017Article
- Article
- Computational learning on specificity-determining residue-nucleotide interactions.Nucleic acids research · 2015Article
- A primer to frequent itemset mining for bioinformatics.Briefings in bioinformatics · 2015Review
- icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2014Article
- DNA motif elucidation using belief propagation.Nucleic acids research · 2013Article
- Fast rule-based bioactivity prediction using associative classification mining.Journal of cheminformatics · 2012Article
- Subtypes of associated protein-DNA (Transcription Factor-Transcription Factor Binding Site) patterns.Nucleic acids research · 2012Article
- Biomedical application of fuzzy association rules for identifying breast cancer biomarkers.Medical & biological engineering & computing · 2012Article
- Discovering associations in biomedical datasets by link-based associative classifier (LAC).PloS one · 2012Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play an essential role in transcriptional regulation. Over the past decades, significant efforts have been made to study the principles for protein-DNA bindings. However, it is considered that there are no simple one-to-one rules between amino acids and nucleotides. Many methods impose complicated features beyond sequence patterns. Protein-DNA bindings are formed from associated amino acid and nucleotide sequence pairs, which determine many functional characteristics. Therefore, it is desirable to investigate associated sequence patterns between TFs and TFBSs. With increasing computational power, availability of massive experimental databases on DNA and proteins, and mature data mining techniques, we propose a framework to discover associated TF-TFBS binding sequence patterns in the most explicit and interpretable form from TRANSFAC. The framework is based on association rule mining with Apriori algorithm. The patterns found are evaluated by quantitative measurements at several levels on TRANSFAC. With further independent verifications from literatures, Protein Data Bank and homology modeling, there are strong evidences that the patterns discovered reveal real TF-TFBS bindings across different TFs and TFBSs, which can drive for further knowledge to better understand TF-TFBS bindings.
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Registered trials
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