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

Protein Families02:47

Protein Families

15.4K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
15.4K
Proteomics01:33

Proteomics

7.5K
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...
7.5K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K

You might also read

Related Articles

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

Sort by
Same author

predALZ: An Ensemble Learning Framework for Identifying Genetic Biomarkers in Familial Alzheimer's Disease.

Current drug targets·2026
Same author

XCPP: A Multi-model Explainable Deep Learning Framework for Accurate Identification of Cell-Penetrating Peptides from Structured Sequence Features.

Current drug targets·2026
Same author

Deep learning based prediction of RNA 5hmC modifications using composite feature representations and comparative benchmarking with transformer models.

BioData mining·2026
Same author

Visible light photocatalytic ammonia production on single Cu entities attached to nitrogen-deficient functionalized BN sheets.

Nanoscale·2026
Same author

An Adaptive Transfer Learning Framework for Multimodal Autism Spectrum Disorder Diagnosis.

Life (Basel, Switzerland)·2025
Same author

An ensemble strategy for piRNA identification through hybrid moment-based feature modeling.

Scientific reports·2025

Related Experiment Video

Updated: Jul 23, 2025

Prediction of Red Blood Cell Antibody Significance Using the Monocyte-Macrophage Assay
11:27

Prediction of Red Blood Cell Antibody Significance Using the Monocyte-Macrophage Assay

Published on: February 7, 2025

540

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based

Gulnaz Perveen1, Fahad Alturise2, Tamim Alkhalifah2

  • 1Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan.

Digital Health
|July 12, 2023
PubMed
Summary

A new computational method, Hemolytic-Pred, accurately identifies hemolytic proteins using sequence data and machine learning. This tool aids in the early detection of hemolytic cells and related disorders.

Keywords:
HemolysisXGBoostcomputational biologyhemolytic proteinsmachine learningmathematical model‌statistical moments

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

15.0K

Related Experiment Videos

Last Updated: Jul 23, 2025

Prediction of Red Blood Cell Antibody Significance Using the Monocyte-Macrophage Assay
11:27

Prediction of Red Blood Cell Antibody Significance Using the Monocyte-Macrophage Assay

Published on: February 7, 2025

540
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

15.0K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Proteomics

Background:

  • Hemolytic proteins play a crucial role in various biological processes and diseases.
  • Accurate identification of hemolytic proteins is essential for understanding disease mechanisms and developing diagnostics.
  • Existing methods for identifying hemolytic proteins may have limitations in terms of speed and accuracy.

Purpose of the Study:

  • To develop and validate a novel in-silico method, Hemolytic-Pred, for predicting hemolytic proteins based on amino acid sequences.
  • To leverage statistical moment-based features and sequence information for enhanced prediction accuracy.
  • To provide a publicly accessible webserver for the practical application of the developed method.

Main Methods:

  • Protein sequences were converted into feature vectors using statistical moment-based features, incorporating position-relative and frequency-relative information.
  • Multiple machine learning algorithms were trained and evaluated for their efficacy in classifying hemolytic proteins.
  • Rigorous validation was performed using self-consistency, 10-fold cross-validation, Jackknife, and independent set tests.

Main Results:

  • The XGBoost classifier demonstrated superior performance, achieving high accuracy across all validation methods (e.g., 0.99 for self-consistency test).
  • Hemolytic-Pred, utilizing the XGBoost classifier, proved to be a robust and efficient solution for predicting hemolytic proteins.
  • The computational models achieved excellent predictive performance, highlighting the effectiveness of the chosen features and algorithms.

Conclusions:

  • Hemolytic-Pred, powered by the XGBoost classifier, serves as a reliable tool for the rapid identification of hemolytic proteins.
  • The method facilitates timely diagnosis of disorders associated with hemolytic cells, offering significant potential benefits in clinical settings.
  • This in-silico approach provides a valuable resource for researchers and clinicians involved in the study and management of hemolytic conditions.