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

Methods of Classification and Identification01:28

Methods of Classification and Identification

1.2K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
1.2K
Machines01:19

Machines

577
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...
577
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

404
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
404
Absolute and Local Extreme Values01:22

Absolute and Local Extreme Values

68
The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
68
Machines: Problem Solving II01:30

Machines: Problem Solving II

666
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.
666
Machines: Problem Solving I01:22

Machines: Problem Solving I

712
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
712

You might also read

Related Articles

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

Sort by
Same author

Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.

Bioinformatics advances·2026
Same author

RPI-PLMGNN: Enhancing RNA-Protein Interaction Prediction with the Pretrained Large Language Models and Graph Neural Networks.

ACS synthetic biology·2026
Same author

Risk prediction models of compassion fatigue among nurses: A systematic review and meta-analysis.

International journal of nursing studies advances·2026
Same author

MPMFMol: Multitask Self-Supervised Pretraining with Multimodal Fine-Tuning for Molecular Property Prediction.

Journal of chemical information and modeling·2026
Same author

Quantum computing applications in drug discovery.

Briefings in bioinformatics·2026
Same author

Foliar Application of Red-Emitting Carbon Dots Promotes Melatonin Biosynthesis and K<sup>+</sup> Homeostasis Enhancing Drought Tolerance in Sweetpotato.

Journal of agricultural and food chemistry·2026
Same journal

Proteomic Profiling of Extracellular Vesicle-Enriched Plasma Using Mag-Net for Biomarker Discovery in Pancreatic Ductal Adenocarcinoma.

Journal of proteome research·2026
Same journal

Computationally Efficient Bayesian Estimation of Graphical Networks for Omics Data.

Journal of proteome research·2026
Same journal

Hierarchy of MS-Based Evidence.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Exosomes from HPV-Positive and HPV-Negative Head and Neck Squamous Cell Carcinoma: Selective Cargo Packaging.

Journal of proteome research·2026
Same journal

Proteomic Analysis Identifies ATE1-Dependent Arginylation Dysregulation across Meningioma Grades.

Journal of proteome research·2026
Same journal

Proteomic Impact of Peripheral Expression of Mutant Huntingtin in <i>C. elegans</i>.

Journal of proteome research·2026
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K

ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm.

Yanjuan Li1, Mengting Niu1, Quan Zou2,3

  • 1School of Information and Computer Engineering , Northeast Forestry University , Harbin 150040 , China.

Journal of Proteome Research
|January 31, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning approach for accurately identifying major histocompatibility complex (MHC) molecules and their subtypes (MHC I and MHC II). The developed method achieved high accuracy, offering a valuable tool for immunological research and diagnostics.

Keywords:
MHC IMHC IIextreme learning machineidentificationmachine learningmajor histocompatibility complex

More Related Videos

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

15.6K
Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

15.1K

Related Experiment Videos

Last Updated: Jan 30, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
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

15.6K
Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

15.1K

Area of Science:

  • Immunoinformatics
  • Computational Biology
  • Machine Learning in Immunology

Background:

  • The Major Histocompatibility Complex (MHC) plays a crucial role in immune responses, including transplantation, autoimmunity, infection, and cancer immunotherapy.
  • Accurate recognition of MHC molecules is essential for understanding and manipulating immune functions.
  • Traditional methods for MHC identification can be labor-intensive and may lack precision.

Purpose of the Study:

  • To develop a novel, accurate, and efficient computational method for identifying Major Histocompatibility Complex (MHC) molecules.
  • To differentiate between MHC class I and MHC class II molecules using machine learning.
  • To provide an accessible online tool for MHC identification.

Main Methods:

  • Utilized a combination of machine learning algorithms and bioinformatics analysis.
  • Employed mixed feature representation methods: SVMProt 188D, bag-of-ngrams (BonG), and information theory (IT).
  • Implemented an extreme learning machine (ELM) classifier with a lin-kernel activation function, validated using 10-fold cross-validation and an independent test set.

Main Results:

  • The proposed algorithm achieved 91.66% accuracy in identifying MHC molecules.
  • Achieved 94.442% accuracy in classifying MHC I and MHC II subtypes.
  • An online web server, ELM-MHC, was developed and made publicly available.

Conclusions:

  • The developed ELM-based method offers a significant improvement in the accuracy and efficiency of MHC identification compared to traditional approaches.
  • The tool is capable of simultaneously identifying MHC molecules and classifying them into MHC I and MHC II categories.
  • The ELM-MHC web server provides a valuable resource for researchers in immunology and related fields.