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

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

8.2K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
8.2K
Peptide Bonds02:43

Peptide Bonds

82.6K
A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
82.6K
Machines01:19

Machines

563
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
563
Self-Help Support Groups01:28

Self-Help Support Groups

341
Self-help support groups are voluntary, community-based organizations that provide a platform for individuals with shared concerns to exchange support, insights, and practical strategies for coping with life challenges. Typically led by group members or paraprofessionals, these groups form a cornerstone of mental health care, especially in reaching populations that are underserved by traditional healthcare systems.
Accessibility and Cost-Effectiveness
One of the primary strengths of self-help...
341
Directing Effect of Substituents: meta-Directing Groups01:09

Directing Effect of Substituents: meta-Directing Groups

5.9K
Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
5.9K
Machines: Problem Solving II01:30

Machines: Problem Solving II

652
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
652

You might also read

Related Articles

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

Sort by
Same author

SSEL-CPP: A SHAP-based feature-selection ensemble learning framework identifies molecular properties of cell-penetrating peptides.

Protein science : a publication of the Protein Society·2026
Same author

Corrigendum to "GinDB-AI: An integrated database of Panax-derived compounds and an AI-driven platform for multidimensional information and biological activity prediction" [J Ginseng Res 50/3 (2026) 100986].

Journal of ginseng research·2026
Same author

CONTRA-IL6: an interpretable hybrid convolutional neural network and Transformer framework for accurate prediction of interleukin-6-inducing peptides using protein language models.

Briefings in bioinformatics·2026
Same author

Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.

Pharmaceutics·2026
Same author

GinDB-AI: An integrated ginsenoside database and AI-driven platform for multidimensional information and biological activity prediction.

Journal of ginseng research·2026
Same author

DeepTYLCV: An interpretable and experimentally validated AI model for predicting virulence of different tomato yellow leaf curl virus strains.

Plant communications·2026

Related Experiment Video

Updated: Jan 26, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.

Vinothini Boopathi1, Sathiyamoorthy Subramaniyam2,3, Adeel Malik4

  • 1Graduate School of Biotechnology, College of Life Science, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Korea. vinothini9327@gmail.com.

International Journal of Molecular Sciences
|April 25, 2019
PubMed
Summary

This study introduces mACPpred, a novel machine learning model for accurately predicting anticancer peptides (ACPs). The new method improves upon existing techniques, offering better performance in identifying potential cancer therapeutics.

Keywords:
anticancer peptidesfeature selectionoptimal featuressequential forward searchsupport vector machine

More Related Videos

Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

13.0K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.6K

Related Experiment Videos

Last Updated: Jan 26, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

13.0K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.6K

Area of Science:

  • Immunoinformatics
  • Computational Biology
  • Machine Learning in Drug Discovery

Background:

  • Anticancer peptides (ACPs) show therapeutic potential for cancer treatment.
  • Accurate prediction of ACPs is crucial but remains challenging.
  • Machine learning offers promising tools but existing methods require improvement.

Purpose of the Study:

  • To develop a novel and accurate prediction model for anticancer peptides (ACPs).
  • To enhance the prediction performance beyond existing computational methods.

Main Methods:

  • A two-step feature selection protocol was applied to seven diverse sequence-based feature encodings.
  • Optimal feature subsets were identified, and their predicted probabilities were used as new feature vectors.
  • A support vector machine (SVM) model, termed mACPpred, was developed using these probability feature vectors.

Main Results:

  • The proposed predictor (mACPpred) demonstrated significantly improved performance compared to models based on individual feature encodings.
  • mACPpred achieved superior accuracy on an independent dataset when compared to existing state-of-the-art methods.
  • The novel feature engineering approach effectively enhanced prediction capabilities.

Conclusions:

  • The mACPpred model represents a significant advancement in the computational prediction of anticancer peptides.
  • This approach offers a more reliable tool for identifying potential ACP drug candidates.
  • The findings contribute to the field of immunoinformatics and accelerate anticancer peptide discovery.