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

Genomics02:02

Genomics

39.6K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
39.6K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.8K
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...
6.8K
Next-generation Sequencing03:00

Next-generation Sequencing

97.6K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
97.6K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

20.5K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
20.5K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.1K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.1K
The Central Dogma01:20

The Central Dogma

31.6K
The central dogma explains the flow of genetic information from DNA nucleotides to the amino acid sequence of proteins.
RNA is the Missing Link Between DNA and Proteins
In the early 1900s, scientists discovered that DNA stores all the information needed for cellular functions and that proteins perform most of these functions. However, the mechanisms of converting genetic information into functional proteins remained unknown for many years. Initially, it was believed that a single gene is...
31.6K

You might also read

Related Articles

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

Sort by
Same author

Low concentrations of tetrasodium EDTA cause significant killing of biofilm-associated Pseudomonas aeruginosa in high-validity models of chronic wound and cystic fibrosis lung infections - but not in a model of endotracheal tube colonization.

Access microbiology·2026
Same author

Clinical applications of digital twin technology in In Vitro Fertilisation.

Journal of gynecology obstetrics and human reproduction·2026
Same author

Composition analysis, QSAR, molecular docking, molecular dynamics simulation and ADMET profiles of Citrus aurantium fruit essential oil compounds as inhibitors of NF-κB and MAPK in brain injury.

Computational biology and chemistry·2026
Same author

Mitigating algorithmic bias in AI-powered toxicology: Frameworks for explainable and equitable predictions in human health and environmental safety.

Toxicology letters·2026
Same author

From Cryptic Clade to Emerging Pathogen: Exploring the Evolutionary Divergence and Clinical Relevance of <i>Escherichia marmotae</i>.

Microorganisms·2026
Same author

Digital twin technology in forensic mental health.

Journal of forensic and legal medicine·2026

Related Experiment Video

Updated: Jan 9, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.

David B Olawade1,2,3, Ayomikun Kade4, Eghosasere Egbon5

  • 1Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK. d.olawade@yorksj.ac.uk.

Molecular Genetics and Genomics : MGG
|December 5, 2025
PubMed
Summary

Artificial intelligence (AI) is revolutionizing genomic data analysis, enhancing accuracy in tasks like variant calling and disease prediction. Addressing challenges in interpretability and ethics is key for AI

Keywords:
Artificial intelligenceDeep learningGenomic data analysisMachine learningMulti-omics integrationPersonalized medicine

More Related Videos

Microbial Communities in Nature and Laboratory - Interview
29:13

Microbial Communities in Nature and Laboratory - Interview

Published on: May 28, 2007

6.7K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.2K

Related Experiment Videos

Last Updated: Jan 9, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K
Microbial Communities in Nature and Laboratory - Interview
29:13

Microbial Communities in Nature and Laboratory - Interview

Published on: May 28, 2007

6.7K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Exponential growth in next-generation sequencing data necessitates advanced computational tools.
  • Traditional bioinformatics methods face limitations in processing complex, multi-dimensional genomic information.

Purpose of the Study:

  • To review the transformative impact of artificial intelligence (AI) on genomic data analysis.
  • To synthesize current AI applications and evaluate emerging technologies in genomics.

Main Methods:

  • Comprehensive literature search of PubMed, Scopus, and Google Scholar (2010-2024).
  • Analysis of AI applications across the genomic analysis pipeline, including variant calling, multi-omics integration, and personalized medicine.

Main Results:

  • AI, particularly deep learning, significantly improves accuracy in variant calling, gene expression profiling, and disease risk prediction.
  • Explainable AI enhances clinical adoption by addressing the 'black box' issue; federated learning enables privacy-preserving research.

Conclusions:

  • AI is a transformative force in genomic research, but clinical translation requires addressing data standardization, interpretability, and ethical concerns.
  • Interdisciplinary collaboration and robust validation are crucial for responsible AI implementation in genomics.