Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An O(N2) algorithm for discovering optimal Boolean pattern pairs.

Hideo Bannai1, Heikki Hyyrö, Ayumi Shinohara

  • 1Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. bannai@ims.u-tokyo.ac.jp

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 21, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction.

Nucleic acids research·2026
Same author

PhenoNMF: A novel multi-layer matrix factorization framework for age-stratified comprehensive phenotypic similarity analysis.

BMC medical informatics and decision making·2026
Same author

Toward Graph-Based Decoding of Tumor Evolution: Spatial Inference of Copy Number Variations.

Diagnostics (Basel, Switzerland)·2025
Same author

Establishing the Asia & Pacific Bioinformatics Joint Congress: a historic milestone in regional bioinformatics collaboration.

Briefings in bioinformatics·2025
Same author

Acceleration of FM-index Queries Through Prefix-free Parsing.

Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop)·2025
Same author

DiffusionST: a deep generative diffusion model-based framework for enhancing spatial transcriptomics data quality and identifying spatial domains.

Briefings in bioinformatics·2025
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

This study introduces an efficient algorithm to find optimal pattern combinations in sequences, correlating them with numeric attributes. The method identifies cooperating or competing regulatory elements in mRNA decay, enhancing biological insights.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Characterizing sets of strings with associated numeric attributes is a key challenge.
  • Identifying patterns that correlate with numeric values is crucial for biological sequence analysis.
  • Understanding regulatory elements in mRNA decay requires analyzing complex pattern interactions.

Purpose of the Study:

  • To develop an efficient algorithm for finding optimal combinations of string patterns.
  • To score pattern combinations based on their correlation with numeric attribute values.
  • To apply the algorithm to identify cooperating, complementing, or competing regulatory elements in mRNA decay.

Main Methods:

  • An O(N^2) time algorithm is presented for optimal pairs of substring patterns combined with Boolean functions.

Related Experiment Videos

  • The algorithm explores all Boolean combinations, including 'p and not q' logic.
  • An efficient implementation utilizes suffix arrays, adaptable for k-pattern combinations in O(Nk) time.

    • Main Results:

    • The algorithm successfully identifies optimal pattern combinations with high correlation to numeric attributes.
    • Demonstrated efficiency with an O(N^2) approach for pairs and O(Nk) for k-patterns.
    • Application to mRNA sequence data revealed insights into regulatory element interactions affecting mRNA decay.

    Conclusions:

    • The developed algorithm provides an efficient method for discovering complex pattern relationships in sequence data.
    • This approach facilitates the identification of functional regulatory elements involved in mRNA decay.
    • The findings contribute to a deeper understanding of gene regulation and molecular mechanisms.