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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

15.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Related Experiment Video

Updated: Apr 4, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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LateBiclustering: Efficient Heuristic Algorithm for Time-Lagged Bicluster Identification.

Joana P Gonçalves, Sara C Madeira

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
    PubMed
    Summary
    This summary is machine-generated.

    LateBiclustering efficiently identifies time-lagged gene expression patterns, revealing biological insights. This heuristic algorithm addresses challenges in discovering transcriptional cascades in temporal data.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Temporal data analysis is crucial for understanding complex biological processes and disease mechanisms.
    • Gene expression time series data offers insights into gene function and dynamics.
    • Traditional clustering methods capture global patterns, while biclustering excels at local patterns, especially in temporal data.

    Purpose of the Study:

    • To address the challenge of identifying time-lagged biclusters in gene expression data, indicative of transcriptional cascades.
    • To develop an efficient algorithm for discovering these complex temporal patterns.

    Main Methods:

    • Proposed LateBiclustering, a heuristic algorithm designed for efficient discovery of time-lagged biclusters.
    • The algorithm offers a polynomial-time solution, overcoming the NP-hard nature of general biclustering.
    • Applied the algorithm to analyze gene expression data from Saccharomyces cerevisiae.

    Main Results:

    • Successfully identified meaningful time-lagged biclusters.
    • These biclusters were relevant to the response of Saccharomyces cerevisiae to heat stress.
    • Demonstrated the algorithm's capability to handle the combinatorial complexity of delayed pattern discovery.

    Conclusions:

    • LateBiclustering provides an efficient and effective method for uncovering time-lagged patterns in biological time series data.
    • The identified patterns offer valuable insights into biological processes, such as stress responses and transcriptional regulation.
    • This approach advances the study of gene dynamics and disease-related perturbations.