ArticleProceedings of the National Academy of Sciences of the United States of America2013
Statistical significance of combinatorial regulations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods.Scientific reports · 2018Trial
- An interpretable combinatorial data-mining framework for predicting new-onset hypertension in the general population.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Distinct radial glia subtypes regulate midbrain dopaminergic neuron development.Nature neuroscience · 2026Article
- Automated Discovery of Reactive Events via Hypergraph Mining of Ab Initio Atomistic Simulations.Journal of chemical theory and computation · 2026Article
- Finding the needle in the haystack-An interpretable sequential pattern mining method for classification problems.Frontiers in big data · 2025Article
- Identification of epistatic SNP combinations in rheumatoid arthritis using LAMPLINK and Japanese cohorts.Journal of human genetics · 2024Article
- Assessing the limitations of relief-based algorithms in detecting higher-order interactions.BioData mining · 2024Article
- Refining the genetic risk of breast cancer with rare haplotypes and pattern mining.Life science alliance · 2023Article
- Higher-order genetic interaction discovery with network-based biological priors.Bioinformatics (Oxford, England) · 2023Article
- Predicting heart failure onset in the general population using a novel data-mining artificial intelligence method.Scientific reports · 2023Article
- Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.Frontiers in immunology · 2023Article
- Overview of frequent pattern mining.Genomics & informatics · 2022Article
- CALDERA: finding all significant de Bruijn subgraphs for bacterial GWAS.Bioinformatics (Oxford, England) · 2022Article
- Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.The Plant cell · 2022Article
- Network-guided search for genetic heterogeneity between gene pairs.Bioinformatics (Oxford, England) · 2021Article
- Enhancing statistical power in temporal biomarker discovery through representative shapelet mining.Bioinformatics (Oxford, England) · 2020Article
- Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms.Scientific reports · 2020Article
- A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce.Genes · 2020Article
- CASMAP: detection of statistically significant combinations of SNPs in association mapping.Bioinformatics (Oxford, England) · 2019Article
- Clinical Implications of Single Nucleotide Polymorphisms in Diagnosis of Asthma and its Subtypes.Yonsei medical journal · 2019Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
More than three transcription factors often work together to enable cells to respond to various signals. The detection of combinatorial regulation by multiple transcription factors, however, is not only computationally nontrivial but also extremely unlikely because of multiple testing correction. The exponential growth in the number of tests forces us to set a strict limit on the maximum arity. Here, we propose an efficient branch-and-bound algorithm called the "limitless arity multiple-testing procedure" (LAMP) to count the exact number of testable combinations and calibrate the Bonferroni factor to the smallest possible value. LAMP lists significant combinations without any limit, whereas the family-wise error rate is rigorously controlled under the threshold. In the human breast cancer transcriptome, LAMP discovered statistically significant combinations of as many as eight binding motifs. This method may contribute to uncover pathways regulated in a coordinated fashion and find hidden associations in heterogeneous data.
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