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

Multiple peak alignment in sequential data analysis: a scale-space-based approach.

Weichuan Yu1, Xiaoye Li, Junfeng Liu

  • 1Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Sai Kung, Kowloon, Hong Kong. eeyu@ust.hk

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 20, 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

Altered amygdala resting-state functional connectivity in anxiety disorders: a coordinate-based meta-analysis.

Psychological medicine·2026
Same author

Comparative biomechanical and molecular mechanisms shaping erect and prostrate winter growth habits in wheat.

Plant science : an international journal of experimental plant biology·2026
Same author

Switchgrass transcription factor PvATAF2 plays positive roles in plant root-zone low-temperature tolerance.

International journal of biological macromolecules·2026
Same author

Deteriorated Gray Matter Connectome in Diabetic Kidney Disease: A Graph Theory Analysis of Individual-Level Gray Matter Morphological Networks.

Brain and behavior·2025
Same author

Divergent structural and functional brain alterations in HIV-infected patients: a multimodal meta-analysis.

Frontiers in neurology·2025
Same author

Transcriptome, Metabolome, and Physiological Analysis Provide New Insights into the Mechanism of Prostrate/Erect Growth Habits in the Wheat Overwintering Period.

Journal of agricultural and food chemistry·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 a novel Gaussian scale-space approach for aligning multiple peaks in sequential data. The method effectively identifies common peak locations from varied datasets, improving peak detection accuracy.

Area of Science:

  • Data analysis
  • Computational biology
  • Signal processing

Background:

  • Multiple peak alignment is crucial for analyzing sequential data.
  • Existing methods may struggle with variations in peak detection.
  • Accurate peak localization is essential for reliable data interpretation.

Purpose of the Study:

  • To develop a robust method for multiple peak alignment in sequential data analysis.
  • To leverage Gaussian scale-space theory for improved peak localization.
  • To enhance peak detection by moving beyond binary classification.

Main Methods:

  • Utilized Gaussian scale-space theory to model peak distributions.
  • Converted peak alignment into a scale-space local maxima search problem.

Related Experiment Videos

  • Employed energy minimization for scale parameter optimization.
  • Developed a quantitative scoring measure for peak candidacy.

    • Main Results:

    • The proposed Gaussian scale-space method demonstrates effective multiple peak alignment.
    • Performance was validated against hierarchical clustering using simulated and real mass spectrometry data.
    • The quantitative scoring approach improved upon binary peak detection.

    Conclusions:

    • The Gaussian scale-space approach offers a powerful solution for the multiple peak alignment problem.
    • This method provides a more nuanced and accurate way to identify common peaks in sequential data.
    • The quantitative scoring enhances the reliability of peak detection in complex datasets.