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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...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: May 13, 2026

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

A practical data processing workflow for multi-OMICS projects.

Michael Kohl1, Dominik A Megger, Martin Trippler

  • 1Medizinisches Proteom-Center, Ruhr-Universitaet Bochum, Universitaetsstrasse 150, D-44801 Bochum, Germany.

Biochimica Et Biophysica Acta
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

Integrating multi-omics data, like transcriptomics and proteomics, is key for understanding biological systems and finding new liver disease biomarkers. Simple linear regression is insufficient for deep analysis, necessitating advanced statistical methods.

Keywords:
(X)PlatComASCIIAmerican Standard Code for Information InterchangeBC-FCBGBiomarkerBox–Cox-transformed fold changesCRANCrossPlatformCommanderD(eucl)DIGE-LC-MS-Transcriptomics overlapData processing workflowEuclidean distanceFCGUIHCCHGNCHUGO Gene Nomenclature CommitteeKEGGKyoto Encyclopedia of Genes and GenomesLDMeSHMedical Subject HeadingsMulti-OMICSOL(DLCT)Quantitative ProteomicsQuantitative TranscriptomicsRAIDRegression analysisThe Comprehensive R Archive Networkbio-molecule groupfold changegraphical user interfacehepatocellular carcinomaliver diseaseredundant array of independent disks

Related Experiment Videos

Last Updated: May 13, 2026

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:

  • Computational biology
  • Systems biology
  • Biomarker discovery

Background:

  • Multi-omics approaches integrate diverse molecular data to understand biological systems.
  • Challenges exist in data processing and integration for multi-omics studies.
  • The PROFILE project focuses on identifying biomarkers and therapeutic targets for liver diseases.

Purpose of the Study:

  • To present a data processing workflow for multi-omics data integration.
  • To introduce the CrossPlatformCommander software for semi-automatic workflow facilitation.
  • To apply and evaluate methods for biomarker detection and analysis using transcriptomics and proteomics data.

Main Methods:

  • Development and application of a multi-omics data processing workflow.
  • Utilizing the CrossPlatformCommander software for data integration and analysis.
  • Performing linear regression analysis on transcriptomics and proteomics data for hepatocellular carcinoma patients.

Main Results:

  • A data processing workflow and supporting software (CrossPlatformCommander) were developed.
  • Linear regression analysis showed limitations in capturing complex transcriptomics-proteomics relationships.
  • The study highlights the need for advanced statistical approaches for robust multi-omics data integration.

Conclusions:

  • Effective multi-omics data integration requires sophisticated statistical methods beyond simple linear regression.
  • The presented workflow and software are adaptable for various high-throughput techniques and omics studies.
  • Further development will focus on integrating multivariate variable selection and classification approaches.