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

Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

769
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
769
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

593
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
593
Machines01:19

Machines

573
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...
573
Machines: Problem Solving II01:30

Machines: Problem Solving II

661
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.
661
Machines: Problem Solving I01:22

Machines: Problem Solving I

709
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...
709
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Global knockout of melanoma differentiation-associated protein 5 protects mice from chronic hypoxia/SU5416-induced pulmonary hypertension.

American journal of physiology. Lung cellular and molecular physiology·2026
Same author

SenSet defines cell-type specific senescence signatures in the aged human lung.

The EMBO journal·2026
Same author

Enhanced antitumor efficacy of combined targeting of adenosine A<sub>2B</sub> receptor and PD-1 is mediated via multiple effects on different cell populations within tumor microenvironment.

Cancer immunology, immunotherapy : CII·2026
Same author

A multi-agent platform for assessment and improvement of bioinformatics software documentation.

bioRxiv : the preprint server for biology·2026
Same author

Transcriptional and epigenetic repression of hematopoietic stem cells underlies bone marrow failure after spinal cord injury.

bioRxiv : the preprint server for biology·2025
Same author

Activation of USP30 Disrupts Endothelial Cell Function and Aggravates Acute Lung Injury Through Regulating the S-Adenosylmethionine Cycle.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same journal

Serum vitamin D level and its association with vertigo frequency and severity in Meniere disease.

Scientific reports·2026
Same journal

PFA-Net: a physics-informed feature enhancement and attention network for interpretable bearing fault diagnosis under strong noise.

Scientific reports·2026
Same journal

Circulating inflammatory, redox, and apoptosis-related alterations in drug-naive idiopathic pulmonary fibrosis: an exploratory case-control study.

Scientific reports·2026
Same journal

A baseline-oriented dynamic aggregation approach for demand-side heterogeneous controllable resources.

Scientific reports·2026
Same journal

Temporal precision and accuracy in schizophrenia: an exploratory study.

Scientific reports·2026
Same journal

Prefrontal EEG spectral and nonlinear signatures of subthreshold depression during resting state and affectively valenced picture/video viewing: a participant-level analysis.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model.

Yan Wang1, Sen Yang1, Jing Zhao2,3

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

Scientific Reports
|March 14, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces the Multi-Features Relatedness (MFR) model to accurately measure conditional gene-gene relatedness by combining expression and prior-knowledge similarities. MFR improves gene interaction prediction and biological network analysis.

More Related Videos

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

7.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

952

Related Experiment Videos

Last Updated: Jan 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
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

7.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

952

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in genomics

Background:

  • Assessing gene-gene relatedness is crucial but challenging, with expression similarities yielding high false discovery rates.
  • Prior-knowledge similarities are limited to global relatedness, necessitating a more integrated approach.

Purpose of the Study:

  • To develop a novel machine learning model, Multi-Features Relatedness (MFR), for accurate conditional gene-gene relatedness measurement.
  • To integrate gene expression similarities with prior-knowledge based similarities for enhanced gene interaction prediction.

Main Methods:

  • Proposed the Multi-Features Relatedness (MFR) machine learning model.
  • Incorporated gene expression similarities and prior-knowledge based similarities into an assessment criterion.
  • Validated MFR using 10-fold cross-validation and test verification on gene-gene interaction datasets (COXPRESdb, KEGG, HPRD, TRRUST, GeneFriends, DIP).

Main Results:

  • MFR achieved the highest area under curve (AUC) values for identifying gene-gene interactions across multiple datasets.
  • Demonstrated an average precision improvement of 1.1% for detecting gene pairs with high expression and prior-knowledge similarities.
  • Outperformed linear models and coexpression analysis methods in gene interaction prediction.

Conclusions:

  • MFR provides a significant advancement in measuring conditional gene-gene relatedness.
  • The model enhances the accuracy and biological significance of gene network construction and gene function prediction.
  • MFR offers improved performance over existing methods for identifying gene-gene interactions.