Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

129
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
129
Genomics02:02

Genomics

36.5K
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...
36.5K
Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

740
The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
740
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

828
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
828
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

95
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
95
2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

239
Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
239

You might also read

Related Articles

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

Sort by
Same author

Fatty acid amide hydrolase inhibition for treatment of amyotrophic lateral sclerosis.

JCI insight·2026
Same author

Unsupervised deep learning enables blur-free resolution enhancement in two-photon microscopy.

Cell reports methods·2026
Same author

Immunopeptidomics combined with full-length transcriptomics uncovers diverse neoantigens.

Cell reports·2025
Same author

scSurv: a deep generative model for single-cell survival analysis.

Bioinformatics (Oxford, England)·2025
Same author

Microglia Display Heterogeneous Initial Responses to Disseminated Tumor Cells.

Cancer research·2025
Same author

NF-κB Is a Potential Therapeutic Target for Histone Deacetylase Inhibitor-Resistant Cutaneous T-Cell Lymphoma.

Cancer science·2025
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
Same journal

CLABP: a contrastive learning framework integrating protein language models and structural information for antibacterial peptide prediction.

Briefings in bioinformatics·2026
Same journal

Toward the regularization of E value from BLAST similarity search into a dissimilarity measure as distance function, and the metrication of protein sequence space.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jul 22, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Ko Abe1, Teppei Shimamura1,2

  • 1Division of Systems Biology, Nagoya University Graduate School of Medicine, Showa-ku, 466-8550, Nagoya, Japan.

Briefings in Bioinformatics
|July 21, 2023
PubMed
Summary
This summary is machine-generated.

We introduce unified nonnegative matrix factorization (UNMF), a flexible statistical framework for analyzing complex biological data. UNMF simplifies pattern discovery in multi-dimensional omics datasets, even with missing values.

Keywords:
Bayesian statisticsfactor analysisgene expression analysismetagenomemulti-dimensional datanonnegative matrix factorization

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K

Related Experiment Videos

Last Updated: Jul 22, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Factor analysis methods like nonnegative matrix factorization (NMF) are crucial for pattern discovery in multi-dimensional omics data.
  • Traditional methods face challenges with varying data formats, structures, and missing values, necessitating extensive preprocessing.
  • Omics data often exists in tensor form, requiring specialized analytical approaches.

Purpose of the Study:

  • To present a novel statistical framework, unified nonnegative matrix factorization (UNMF), for robust pattern extraction from biological datasets.
  • To develop a user-friendly and unified approach that simplifies data analysis and tool development for omics data.
  • To address limitations of traditional methods concerning data format, structure, missing values, and tensor data.

Main Methods:

  • Developed unified nonnegative matrix factorization (UNMF), a statistical framework designed for tidy data.
  • Implemented UNMF to handle diverse data structures and formats, including tensor data with missing observations and repeated measurements.
  • Applied UNMF to analyze several multi-dimensional omics datasets.

Main Results:

  • UNMF demonstrates ease of use and simplifies data analysis workflows for biological data.
  • The framework effectively handles messy biological datasets, including those with missing values and complex structures.
  • Successful application to multi-dimensional omics data showcases UNMF's capability for pattern discovery and integration.

Conclusions:

  • UNMF offers a unified and user-friendly solution for analyzing complex, multi-dimensional omics data.
  • The framework's flexibility in handling various data formats and missing values makes it highly valuable for life sciences.
  • UNMF provides a powerful tool for advancing pattern discovery and data integration in biological research.