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 Concept Videos

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

You might also read

Related Articles

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

Sort by
Same author

Programmed DNA elimination drives rapid genomic innovation in two thirds of all bird species.

bioRxiv : the preprint server for biology·2025
Same author

Songbird germline-restricted chromosome as a potential arena of genetic conflicts.

Current opinion in genetics & development·2023
Same author

Micro Germline-Restricted Chromosome in Blue Tits: Evidence for Meiotic Functions.

Molecular biology and evolution·2023
Same author

Genomic signatures of the evolution of a diurnal lifestyle in Strigiformes.

G3 (Bethesda, Md.)·2022
Same author

Occasional paternal inheritance of the germline-restricted chromosome in songbirds.

Proceedings of the National Academy of Sciences of the United States of America·2022
Same author

Local selection signals in the genome of blue tits emphasize regulatory and neuronal evolution.

Molecular ecology·2022
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

Related Experiment Video

Updated: Jul 3, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Fine-scale genetic mapping using independent component analysis.

Zaher Dawy1, Michel Sarkis, Joachim Hagenauer

  • 1Department of Electrical and Computer Engineering, American University of Beirut, Riad El Solh 11-0236, Beirut 1107 2020, Lebanon. zaher.dawy@aub.edu.lb

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|August 2, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel genetic mapping method using independent component analysis to identify single nucleotide polymorphisms (SNPs) associated with complex diseases like schizophrenia. The approach reveals both individual SNPs and interacting SNP groups, enhancing disease gene discovery.

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Related Experiment Videos

Last Updated: Jul 3, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Complex diseases arise from multiple genetic loci with unknown interactions.
  • Accurate genetic mapping is crucial for understanding disease etiology and developing treatments.
  • Current methods may not fully capture the intricate genetic architecture of complex traits.

Purpose of the Study:

  • To develop a fine-scale genetic mapping method for complex diseases.
  • To identify both individual single nucleotide polymorphisms (SNPs) and associated SNP groups.
  • To model the relationship between DNA polymorphisms and complex traits using a linear mixing process.

Main Methods:

  • Independent Component Analysis (ICA) applied to a linear mixing model of genetic data.
  • Application to a clinical dataset for Schizophrenia (368 individuals, 42 SNPs).
  • Validation through a simulation study to assess performance and robustness.

Main Results:

  • The proposed ICA-based method successfully identified associated SNPs and independent SNP groups.
  • Demonstrated novel characteristics compared to existing genetic mapping techniques.
  • Evaluated robustness against missing genotype data and small sample sizes.

Conclusions:

  • The ICA-based method offers a powerful new approach for fine-scale genetic mapping of complex diseases.
  • Identifying SNP groups alongside individual SNPs provides a more comprehensive understanding of genetic contributions.
  • The method shows promise for improving disease gene discovery and understanding genetic interactions.