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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...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: Jun 16, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Decoding disease and therapy through multiomics integration and systems analysis.

Mano Joseph Mathew1,2, Joyal Mathew3, Ripsy Merrin Chacko1,4,5,6

  • 1Laboratoire Génomique, Bioinformatique et Chimie Moléculaire, EA7528, Conservatoire National des Arts et Métiers, HESAM Université, 2 Rue Conté, 75003 Paris, Ile de France, France.

Briefings in Bioinformatics
|June 14, 2026
PubMed
Summary
This summary is machine-generated.

Computational multiomics methods, using machine learning, classify patient subtypes and discover biomarkers for precision medicine. This review explores multiomics applications in oncology, ageing, and immune diseases, detailing integration strategies and challenges.

Keywords:
biomarker discoverydata integrationfusion strategiesmultiomics

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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

Published on: September 20, 2024

Area of Science:

  • Computational biology
  • Bioinformatics
  • Translational medicine

Background:

  • High-throughput technologies enable multi-layer molecular profiling.
  • Multiomics approaches enhance understanding of disease mechanisms and heterogeneity.
  • Machine learning underpins computational multiomics methods.

Purpose of the Study:

  • Review current multiomics applications in oncology, ageing, and immune-mediated diseases.
  • Highlight strengths and challenges of integrative multiomics models.
  • Examine integration strategies and computational advancements.

Main Methods:

  • Review of computational multiomics applications.
  • Analysis of integration strategies (early/late fusion, horizontal/vertical).
  • Examination of preprocessing techniques and computational platforms.

Main Results:

  • Multiomics aids in patient stratification, biomarker discovery, and drug repurposing.
  • Integrative models offer deeper insights into disease biology.
  • Various integration strategies exist, each with unique strengths and weaknesses.

Conclusions:

  • Computational multiomics is crucial for advancing precision medicine.
  • Understanding disease heterogeneity requires integrative multiomics approaches.
  • Continued development in computational tools is essential for multiomics success.