Jove
Visualize
Contact Us

Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.1K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.1K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.1K

You might also read

Related Articles

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

Sort by
Same author

Supplementation with effective microorganisms in earthen ponds affects common carp growth and abundance of specific bacterial families.

BMC genomics·2026
Same author

Comparison of BLUPF90IOD3 and MiXBLUP implementations of the single-step model applied to the Polish national dairy cattle evaluation.

The Journal of dairy research·2026
Same author

Are head and neck versus abdominal paragangliomas driven by different single nucleotide events?

Journal of applied genetics·2025
Same author

Exploring the impact of sequence context on errors in SNP genotype calling with whole genome sequencing data using AI-based autoencoder approach.

NAR genomics and bioinformatics·2024
Same author

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

International journal of molecular sciences·2024
Same author

Nextflow vs. plain bash: different approaches to the parallelization of SNP calling from the whole genome sequence data.

NAR genomics and bioinformatics·2024
Same journal

RETRACTED: Kim et al. The Angiogenesis Inhibitor ALS-L1023 from Lemon-Balm Leaves Attenuates High-Fat Diet-Induced Nonalcoholic Fatty Liver Disease Through Regulating the Visceral Adipose-Tissue Function. <i>Int. J. Mol. Sci.</i> 2017, <i>18</i>, 846.

International journal of molecular sciences·2026
Same journal

Correction: Mahmud et al. Thymoquinone Attenuates NF-κβ Signalling Activation in Retinal Pigment Epithelium Cells Under AMD-Mimicking Conditions. <i>Int. J. Mol. Sci.</i> 2025, <i>26</i>, 11473.

International journal of molecular sciences·2026
Same journal

Correction: Borovikov et al. The Twisting and Untwisting of Actin and Tropomyosin Filaments Are Involved in the Molecular Mechanisms of Muscle Contraction, and Their Disruption Can Result in Muscle Disorders. <i>Int. J. Mol. Sci</i>. 2025, <i>26</i>, 6705.

International journal of molecular sciences·2026
Same journal

Correction: Molagoda et al. Flavonoid Glycosides from <i>Ziziphus jujuba</i> var. <i>inermis</i> (Bunge) Rehder Seeds Inhibit α-Melanocyte-Stimulating Hormone-Mediated Melanogenesis. <i>Int. J. Mol. Sci.</i> 2021, <i>22</i>, 7701.

International journal of molecular sciences·2026
Same journal

Correction: Guo et al. Integrated Transcriptomic and Metabolomic Analysis Reveals the Molecular Regulatory Mechanism of Flavonoid Biosynthesis in Maize Roots Under Lead Stress. <i>Int. J. Mol. Sci.</i> 2024, <i>25</i>, 6050.

International journal of molecular sciences·2026
Same journal

Correction: Chang et al. Improvement of Carbon Tetrachloride-Induced Acute Hepatic Failure by Transplantation of Induced Pluripotent Stem Cells Without Reprogramming Factor c-Myc. <i>Int. J. Mol. Sci.</i> 2012, <i>13</i>, 3598-3617.

International journal of molecular sciences·2026
See all related articles
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 Video

Updated: Sep 10, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Feature Selection Strategies for Deep Learning-Based Classification in Ultra-High-Dimensional Genomic Data.

Krzysztof Kotlarz1, Dawid Słomian2, Weronika Zawadzka1

  • 1Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, 51-631 Wroclaw, Poland.

International Journal of Molecular Sciences
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

We developed efficient computational tools for genomic data analysis. Multidimensional Supervised Rank Aggregation (MD-SRA) balances classification accuracy and computational speed for high-dimensional genomic data.

Keywords:
SNPdeep learningdimensionality reductionfeature selection algorithmsmixed linear modelmulti-class classificationwhole-genome sequencing

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Related Experiment Videos

Last Updated: Sep 10, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • High-throughput sequencing generates vast genomic data, posing statistical challenges like the p >> n problem in Whole-Genome Sequencing.
  • Efficient feature selection is crucial for analyzing ultra-high-dimensional genomic datasets.

Purpose of the Study:

  • To address the need for efficient computational and statistical tools for feature selection in high-dimensional genomic data.
  • To evaluate the performance of different feature selection algorithms for breed classification using genomic data.

Main Methods:

  • Applied three feature selection algorithms: SNP-tagging, one-dimensional Supervised Rank Aggregation (1D-SRA), and multidimensional Supervised Rank Aggregation (MD-SRA).
  • Classified 1825 individuals into five breeds using 11,915,233 Single Nucleotide Polymorphisms (SNPs).
  • Utilized a deep learning classifier (Convolutional Neural Networks) for breed classification.

Main Results:

  • SNP-tagging achieved an F1-score of 86.87% with rapid computation.
  • 1D-SRA offered the best classification quality (96.81%) but faced computational, memory, and storage limitations.
  • MD-SRA demonstrated a balance between classification quality (95.12%) and computational efficiency (17x faster, 14x less storage).

Conclusions:

  • MD-SRA is a suitable and efficient approach for classifying high-dimensional data, offering a balance between accuracy and computational resources.
  • SRA-based methods are versatile and applicable beyond genomic data analysis.