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

A clustering algorithm based on two distance functions for MEC model.

Ying Wang1, Enmin Feng, Ruisheng Wang

  • 1Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, China. wwangying2003@yahoo.com.cn

Computational Biology and Chemistry
|March 17, 2007
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

Analysis of the formation mechanism of Rehmannia Glutinosa nodular characteristics based on transcriptome and targeted metabolome.

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

Dietary <i>p</i>-Coumaric Acid Modulates Non-Core Gut Microbiota and Sucrose Solution Consumption in <i>Apis cerana</i>.

Insects·2026
Same author

Investigation of distinct metabolic profiles in the phloem and xylem of Rehmanniae Radix using untargeted metabolomics coupled with quantification of glycosides.

Journal of pharmaceutical and biomedical analysis·2026
Same author

Establishment of HPLC Fingerprint of Different Drying Methods of Salvia miltiorrhiza and Study on Spectrum Effect Relationship.

Biomedical chromatography : BMC·2025
Same author

Short-term dietary phytochemicals extend the longevity of eastern honey bees (Apis cerana [Hymenoptera: Apidae]).

Journal of economic entomology·2025
Same author

Handcrafted Local Feature Descriptor-Based Point Cloud Registration and Its Applications: A Review.

IEEE transactions on visualization and computer graphics·2025
Same journal

Protein dynamic simulations: From early inception to clinical translation over half a century.

Computational biology and chemistry·2026
Same journal

Integrated omics and virtual screening predict Tabularin as a dual inhibitor of the prognostic microRNAs mir-19a and mir-32 in colorectal cancer.

Computational biology and chemistry·2026
Same journal

In silico characterization of acetyl-CoA carboxylase from Staphylococcus aureus and Escherichia coli: A comparative analysis.

Computational biology and chemistry·2026
Same journal

An optimized cascaded transformer with progressive attention for lung and colon cancer diagnosis from histopathological images.

Computational biology and chemistry·2026
Same journal

From cross cancer transcriptomics to therapeutics: WGX-50 target hub genes in breast cancer and non-small cell lung carcinoma.

Computational biology and chemistry·2026
Same journal

Blood-based biomarker discovery through integrative transcriptomic and miRNA network analyses in schizophrenia, major depressive disorder, and bipolar disorder.

Computational biology and chemistry·2026
See all related articles

This study introduces a novel clustering algorithm for haplotype reconstruction using single nucleotide polymorphism (SNP) fragments. The method effectively infers true haplotype pairs by employing two new distance functions for improved accuracy.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Haplotype reconstruction is crucial for understanding genetic variations.
  • Existing methods for inferring haplotypes from single nucleotide polymorphism (SNP) data face challenges with accuracy and efficiency.
  • Short genome fragment assembly generates localized polymorphism data requiring robust reconstruction techniques.

Purpose of the Study:

  • To develop an effective algorithm for haplotype reconstruction from SNP fragments.
  • To introduce novel distance functions for measuring differences and similarities between SNP fragments.
  • To address the Multiple Exactly Cover (MEC) model for haplotype inference.

Main Methods:

  • Development of two novel distance functions to quantify SNP fragment dissimilarity and similarity.

Related Experiment Videos

  • Proposal of a clustering algorithm incorporating these distance functions.
  • The algorithm includes an initial haplotype pair determination and a re-clustering step for refinement.

    • Main Results:

    • The proposed algorithm effectively reconstructs haplotype pairs using the defined distance functions.
    • Comparative analysis demonstrates the algorithm's effectiveness and feasibility in solving the MEC model.
    • The new distance functions enhance the accuracy of SNP fragment clustering.

    Conclusions:

    • The developed clustering algorithm provides a feasible and effective approach for haplotype reconstruction.
    • The novel distance functions are key to the improved performance of the algorithm.
    • This method offers a valuable tool for analyzing localized polymorphism data in genomics.