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Related Concept Videos

Genomics02:02

Genomics

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...
Human Virome01:26

Human Virome

The human body harbors a vast and diverse viral community known as the human virome. The virome includes bacteriophages that infect bacteria, and eukaryotic viruses that infect human cells. Transient dietary and environmental viruses also contribute to this dynamic ecosystem. Estimates suggest the human body may contain on the order of 10¹³ viral particles, though abundance varies widely by body site and detection method.Comprehensive characterization of the virome has become possible only with...
Viruses with RNA Genomes01:29

Viruses with RNA Genomes

RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
Viral Recombination00:57

Viral Recombination

Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...

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Related Experiment Video

Updated: May 24, 2026

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
09:36

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

AI in multi-omics analysis in viral diseases.

Hiteshi Vaidya1, Manoj Kumar2

  • 1Virology Unit, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Chandigarh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.

Progress in Molecular Biology and Translational Science
|May 22, 2026
PubMed
Summary

Artificial intelligence (AI) and multi-omics data integration are revolutionizing viral disease research. AI tools help analyze complex biological data, improving our understanding of virus-host interactions and leading to better diagnostics and treatments.

Keywords:
Artificial intelligenceMachine learningMulti-omicsViral diseaseVirus

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Last Updated: May 24, 2026

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

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Area of Science:

  • Virology
  • Bioinformatics
  • Computational Biology

Background:

  • Viral diseases pose significant global health risks, causing epidemics and long-term complications.
  • Understanding virus-host interactions is complex, requiring advanced analytical methods.
  • Multi-omics datasets (genomics, transcriptomics, proteomics, etc.) offer comprehensive biological insights but are challenging to integrate.

Purpose of the Study:

  • To explore the application of artificial intelligence (AI) in analyzing and interpreting multi-omics data for viral disease research.
  • To highlight how AI-driven multi-omics approaches advance the understanding of viral pathogenesis and host responses.
  • To discuss the potential of integrated multi-omics and AI for improved viral disease diagnosis, treatment, and prevention.

Main Methods:

  • Review of studies applying AI, including machine learning (ML) and network-based approaches, to multi-omics data in viral research.
  • Analysis of AI's role in integrating and interpreting high-dimensional datasets from various omics levels.
  • Examination of case studies involving viruses such as SARS-CoV-2, HIV, and influenza.

Main Results:

  • AI effectively analyzes, integrates, and interprets complex multi-omics data for viral disease studies.
  • AI-assisted multi-omics approaches enhance understanding of virus-induced cellular changes.
  • Identification of biomarkers and design of targeted therapies are facilitated by AI in viral research.

Conclusions:

  • Integrating multi-omics data with AI significantly improves the diagnosis, treatment, and prevention of viral diseases.
  • AI-powered multi-omics research offers deeper insights into virus-host interactions.
  • Addressing challenges in data integration and AI methodology is crucial for future advancements.