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
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

7.8K
If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...
7.8K
Machines01:19

Machines

578
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...
578
Distinctive Features of Adult Stem Cells vs Cancer Stem Cells01:18

Distinctive Features of Adult Stem Cells vs Cancer Stem Cells

4.5K
A stem cell is an unspecialized cell that can divide without limit as needed and can, under specific conditions, differentiate into specialized cells.
Adult stem cells
Adult stem cells are tissue-specific; hence, they divide to develop the tissue from which they originate. One type of adult stem cell is the epithelial stem cell, which gives rise to the keratinocytes in the multiple layers of epithelial cells in the epidermis of the skin. Adult bone marrow has three distinct types of stem cells:...
4.5K
Eukaryotic RNA Polymerases00:58

Eukaryotic RNA Polymerases

27.1K
RNA Polymerase (RNAP) is conserved in all animals, with bacterial, archaeal, and eukaryotic RNAPs sharing significant sequence, structural, and functional similarities. Among the three eukaryotic RNAPs, RNA Polymerase II is most similar to bacterial RNAP in terms of both structural organization and folding topologies of the enzyme subunits. However, these similarities are not reflected in their mechanism of action.
All three eukaryotic RNAPs require specific transcription factors, of which the...
27.1K
RNA Structure01:23

RNA Structure

79.1K
Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
79.1K

You might also read

Related Articles

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

Sort by
Same author

Blood-Activating and Stasis-Removing Chinese Patent Medicine in Perioperative Period of Percutaneous Coronary Intervention for Myocardial Infarction: A Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials.

Journal of evidence-based medicine·2026
Same author

Functional characterization of four glycosyltransferases for biosynthesis of steroidal saponins in medicinal plant <i>Paris polyphylla</i>.

Synthetic and systems biotechnology·2026
Same author

Exploring methodology for investigating Chinese coronary artery disease patient values and preferences: A methodological study protocol.

Global health research and policy·2026
Same author

Cascaded regulatory network composed of small RNAs involves in the symbiosis of Panax notoginseng and fungus Acremonium sp. D212.

Scientific reports·2026
Same author

Blood activating and stasis removing Chinese patent medicine in perioperative period of PCI for myocardial infarction: a protocol for a systematic review and Bayesian network meta-analysis of randomized controlled trials.

Systematic reviews·2026
Same author

Constructing epigenetic regulatory landscapes of plant lncRNAs-an exploration utilizing the novel specialized platform PERlncDB.

The Plant journal : for cell and molecular biology·2025

Related Experiment Video

Updated: Jan 31, 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

2.5K

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated

Xuan Zhang1,2, Jun Wang1,3, Jing Li1

  • 1CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, 666303, Yunnan, People's Republic of China.

BMC Medical Genomics
|January 2, 2019
PubMed
Summary

This study introduces CRlncRC, a novel classifier for identifying cancer-related long noncoding RNAs (lncRNAs). CRlncRC integrates multiple features using machine learning, significantly improving prediction accuracy and identifying new cancer-related lncRNA candidates.

Keywords:
Cancer-relatedClassificationIntegrated featuresLncRNAMachine learning

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

1.2K

Related Experiment Videos

Last Updated: Jan 31, 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

2.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

1.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Long noncoding RNAs (lncRNAs) play a crucial role in cancer initiation and progression.
  • Existing computational methods for identifying cancer-related lncRNAs require improved accuracy and efficiency.
  • Advancements in cancer data and mechanistic understanding necessitate updated prediction algorithms.

Purpose of the Study:

  • To develop a novel and accurate computational method for classifying cancer-related lncRNAs.
  • To integrate diverse features including genomic, expression, epigenetic, and network data.
  • To enhance the prediction accuracy and efficiency of cancer-related lncRNA identification.

Main Methods:

  • Developed CRlncRC, a Cancer-Related lncRNA Classifier.
  • Integrated four categories of features: genomic, expression, epigenetic, and network.
  • Employed five machine learning techniques: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN).
  • Utilized ten-fold cross-validation for model evaluation.

Main Results:

  • Random Forest (RF) demonstrated the highest performance with an AUC of 0.82.
  • Epigenetic and network features were identified as key contributors to classification accuracy.
  • CRlncRC outperformed existing classifiers in sensitivity and specificity.
  • Identified 121 potential cancer-related lncRNA candidates from the TANRIC dataset, many with literature support.

Conclusions:

  • CRlncRC is a powerful and accurate method for identifying cancer-related lncRNAs.
  • The integration of multiple features, particularly epigenetic and network data, significantly improves prediction.
  • RF is the optimal machine learning model for this classification task.
  • The predicted lncRNAs provide valuable candidates for future functional studies in cancer.