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

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,...

You might also read

Related Articles

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

Sort by

Same author

QSPR modeling and multi-criteria ranking of antiviral drugs using degree-based topological indices, artificial neural networks, and TOPSIS.

Computational biology and chemistry·2026

Same author

Artificial intelligence investigation of magneto radiated nanofluid under mixed convection.

Discover nano·2026

Same author

One-argument Lie scaling and sensitivity analysis of UCM liquid flow over a vertical wedge using response surface method.

Scientific reports·2026

Same author

Exploring anticancer drug structures through vertex based resolving parameters.

Scientific reports·2026

Same author

Exploring topological indices and power curve fitting models for predicting heat of formation in magnesium aluminate network.

Scientific reports·2026

Same author

Design and stability analysis of a multi-delay tumor-immune model with adaptive nonlinear feedback control.

Computational biology and chemistry·2026

Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026

Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026

Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026

Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026

Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026

Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 7, 2025

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

On analysis of phthalocyanine network through statistical method.

Hadeel AlQadi¹, Muhammad Farhan Hanif², Mazhar Hussain³

¹Department of Mathematics, College of Science, Jazan University, P.O. Box 114, 45142, Jazan, Kingdom of Saudi Arabia.

Scientific Reports

|December 29, 2024

Summary

This summary is machine-generated.

This study analyzes phthalocyanine derivative nanostructures using topological indices and correlation analysis. It highlights the importance of computational methods for understanding material properties and applications.

Keywords:

Degree of vertex Entropy Indices Pearson correlation coefficient Phthalocyanine

More Related Videos

Preparation of N-2-alkoxyvinylsulfonamides from N-tosyl-1,2,3-triazoles and Subsequent Conversion to Substituted Phthalans and Phenethylamines

Preparation of N-2-alkoxyvinylsulfonamides from N-tosyl-1,2,3-triazoles and Subsequent Conversion to Substituted Phthalans and Phenethylamines

Published on: January 3, 2018

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Related Experiment Videos

Last Updated: May 7, 2025

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

Preparation of N-2-alkoxyvinylsulfonamides from N-tosyl-1,2,3-triazoles and Subsequent Conversion to Substituted Phthalans and Phenethylamines

Preparation of N-2-alkoxyvinylsulfonamides from N-tosyl-1,2,3-triazoles and Subsequent Conversion to Substituted Phthalans and Phenethylamines

Published on: January 3, 2018

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Area of Science:

Materials Science
Computational Chemistry
Nanotechnology

Background:

Phthalocyanine derivative nanostructures are organized organometallic materials with 2D polymeric frameworks.
Understanding their topological and structural characteristics is crucial for materials research.

Purpose of the Study:

To analyze phthalocyanine derivative nanostructures using Zagreb-type indices.
To investigate the relationship between structural features and material properties via Pearson correlation analysis.
To demonstrate the value of computational techniques in materials science.

Main Methods:

Topological analysis using Zagreb-type indices.
Pearson correlation analysis to examine structure-property relationships.
Visualization of index-entropy relationships using a heat map.

Main Results:

Zagreb-type indices provide insights into the molecular topology of phthalocyanine nanostructures.
Pearson correlation analysis reveals significant relationships between structural features and material qualities.
A heat map effectively illustrates the correlation between indices and entropy.

Conclusions:

Computational methods, including topological indices and correlation analysis, are valuable for characterizing phthalocyanine derivatives.
This multidimensional examination aids in understanding the properties and potential applications of these materials in research.