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

2D NMR: Overview of Heteronuclear Correlation Techniques

2D NMR: Overview of Heteronuclear Correlation Techniques

Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...

2D NMR: Overview of Homonuclear Correlation Techniques

2D NMR: Overview of Homonuclear Correlation Techniques

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...

2D NMR: Homonuclear Correlation Spectroscopy (COSY)

2D NMR: Homonuclear Correlation Spectroscopy (COSY)

Homonuclear correlation spectroscopy, or COSY, is a 2-dimensional NMR technique that provides information about coupled protons. Typically, the geminal and vicinal coupling are observed. For example, consider the COSY spectrum of ethyl acetate, where its 1D proton NMR spectrum is plotted along the vertical and horizontal axes with their corresponding chemical shift scale. Three spots on the diagonal corresponding to the three peaks in the 1D proton spectrum are called diagonal peaks. The COSY...

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by

Same author

ClusterGraph: a new tool for visualisation and compression of multidimensional data.

GigaScience·2026

Same author

Publisher Correction: Tumor transcriptional state predicts survival in immune-checkpoint-blockade-treated glioblastoma.

Nature cancer·2026

Same author

Quantum ensembling methods for healthcare and life science.

Briefings in bioinformatics·2026

Same author

Tumor transcriptional state predicts survival in immune-checkpoint-blockade-treated glioblastoma.

Nature cancer·2026

Same author

Publisher Correction: Advancing single-cell omics and cell-based therapeutics with quantum computing.

Nature reviews. Molecular cell biology·2026

Same author

Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

Winter-associated downregulation of ovarian NR5A2 correlates with impaired follicle development in the striped hamster (Cricetulus barabensis).

Scientific reports·2026

Same journal

Both underestimation and overestimation of sleep duration predict mortality in older men with sleep disturbances.

Scientific reports·2026

Same journal

Predicting the flood susceptibility under land use and climate change scenarios using deep learning algorithms.

Scientific reports·2026

Same journal

Progress towards sustainable development and the urban-rural divide: an analysis of municipalities in Japan.

Scientific reports·2026

Same journal

Satellite-based analysis of precipitation across algeria's hydrographic watersheds (1983-2022) and their relationship with climate indices.

Scientific reports·2026

Same journal

Study on acoustic emission and infrared radiation characteristics of coal combination with different tectonic coal thickness.

Scientific reports·2026

See all related articles

Search research articles

Home
Probing Omics Data Via Harmonic Persistent Homology.

Home
Probing Omics Data Via Harmonic Persistent Homology.

Related Experiment Video

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Probing omics data via harmonic persistent homology.

Davide Gurnari¹, Aldo Guzmán-Sáenz², Filippo Utro²

¹Dioscuri Centre in Topological Data Analysis, Mathematical Institute PAN, Warsaw, Poland.

Scientific Reports

|November 6, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Topological data analysis (TDA) using harmonic persistent homology can identify molecular biomarkers in complex diseases. This method reveals hidden patterns in multi-omics data for disease subtyping and biomarker discovery.

Keywords:

Data analysis Multi-omics Pattern discovery Topological data analysis

More Related Videos

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

Published on: October 2, 2012

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Related Experiment Videos

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

Published on: October 2, 2012

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Area of Science:

Computational Biology
Data Science
Genomics

Background:

Identifying molecular signatures in complex diseases with similar symptoms from high-dimensional multi-omics data is challenging.
Topological Data Analysis (TDA) offers a geometric approach to extract higher-order relationships from data.

Purpose of the Study:

To apply harmonic persistent homology to multi-omics data for improved biomarker identification.
To overcome limitations in cycle representation within TDA for homology classes.

Main Methods:

Utilized harmonic persistent homology, a TDA technique, on multi-omics datasets.
Applied the method to analyze the geometric structure of high-dimensional data.

Main Results:

Discovered hidden patterns and relationships between different omic profiles.

Successfully identified biomarkers predictive of disease subtypes in cancer data.

Demonstrated effective dissection of multi-omics data for biomarker discovery.

Conclusions:

Harmonic persistent homology is effective for multi-omics data analysis.
This approach aids in disease subtyping and identifying biomarkers for complex diseases.
The method highlights latent biological pathways associated with disease.