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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...

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Related Experiment Video

Updated: May 24, 2026

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
07:47

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Published on: September 15, 2023

AI in multi-omics analysis in cancer.

Koushikee Ghosh1, Suditi Saha2, Sudipto Saha1

  • 1Department of Biological Sciences, Bose Institute, Kolkata, India.

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

Multi-omics integration is crucial for understanding cancer recurrence and drug resistance in major cancers like lung, breast, colorectal, and prostate. This approach aids in developing artificial intelligence (AI) models for improved cancer prediction and patient outcomes.

Keywords:
AIMulti-omicscancer prognosiscancer risk assessmentcancer stages predictionsurvivability prediction

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Last Updated: May 24, 2026

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

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:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Increasing global prevalence of lung, breast, colorectal, and prostate cancers.
  • Cancer recurrence and drug resistance remain significant clinical challenges.
  • Multi-omics data integration offers potential for advancing cancer research.

Purpose of the Study:

  • To review multi-omics resources for cancer data reanalysis.
  • To discuss the development of AI-based prediction models using multi-omics data.
  • To explore multi-omics integration in major cancer types.

Main Methods:

  • Review of multi-omics integration approaches.
  • Discussion of artificial intelligence (AI) and statistical methods for data analysis.
  • Focus on integrating diverse biological data types (genomics, transcriptomics, etc.).

Main Results:

  • Multi-omics integration is effective for cancer sub-classification.
  • These approaches enhance prediction of patient survival and recurrence.
  • AI-based models show promise in cancer prediction.

Conclusions:

  • Multi-omics integration is vital for addressing cancer recurrence and drug resistance.
  • Available resources facilitate the development of advanced AI prediction models.
  • Further research in multi-omics integration will improve cancer management and patient outcomes.