Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Proteomics

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

MALDI-TOF Mass Spectrometry

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...

You might also read

Related Articles

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

Sort by

Same author

Ancestry informative genetic variants associated with tobacco metabolic and detoxification capacity measured by 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) among smokers.

Archives of toxicology·2026

Same author

Optimizing and Validating Systemic DNA Damage Response Profiling to Predict Neoadjuvant Chemoradiation Response in Rectal Cancer.

medRxiv : the preprint server for health sciences·2024

Same author

Smoking history is associated with reduced efficacy of neoadjuvant therapy in pancreatic adenocarcinoma.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract·2024

Same author

Type of neoadjuvant treatment strategy is associated with differential pathologic responses in pancreatic ductal adenocarcinoma.

American journal of surgery·2023

Same author

A phase 1 study of nivolumab in combination with interferon-gamma for patients with advanced solid tumors.

Nature communications·2023

Same author

Folic Acid Supplementation Promotes Hypomethylation in Both the Inflamed Colonic Mucosa and Colitis-Associated Dysplasia.

Cancers·2023

Search research articles

Related Experiment Video

Updated: Jul 3, 2026

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

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

Published on: September 20, 2024

Nonnegative matrix factorization: an analytical and interpretive tool in computational biology.

Karthik Devarajan¹

¹Division of Population Science, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA. karthik.devarajan@fccc.edu

Plos Computational Biology

|July 26, 2008

Summary

Nonnegative matrix factorization (NMF) is a powerful tool for analyzing large biological datasets from high-throughput technologies. This method aids in molecular pattern discovery and gene expression analysis in computational biology.

More Related Videos

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Jul 3, 2026

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

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

Published on: September 20, 2024

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

High-throughput technologies generate vast biological data, necessitating advanced analytical methods.
Nonnegative matrix factorization (NMF) is a machine learning technique for decomposing data matrices.

Purpose of the Study:

To review Nonnegative Matrix Factorization (NMF) as a data analysis and interpretation tool in computational biology.
To highlight NMF applications in molecular pattern discovery, gene expression analysis, and biomedical informatics.

Main Methods:

Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix V into two nonnegative matrices, W and H.
In gene expression analysis, W defines metagenes and H represents metagene expression patterns.

Main Results:

NMF has been successfully applied to various computational biology tasks.
Applications include molecular pattern discovery, class prediction, and functional gene characterization.

Conclusions:

NMF is a valuable unsupervised learning method for interpreting complex biological data.
Its utility extends across multiple domains within computational biology and biomedical informatics.