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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.8K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
9.8K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.6K
3.6K
Control Volume and System Representations01:16

Control Volume and System Representations

1.5K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.5K
State Space Representation01:27

State Space Representation

541
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
541
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

929
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
929
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

514
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
514

You might also read

Related Articles

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

Sort by
Same author

From acute defense to prolonged metabolic homeostasis: Insights into microalgae responses to PVC microplastic exposure.

Journal of hazardous materials·2026
Same author

Evaluating the Role of Supervised Early Operative Exposure in Improving Surgical Confidence and Patient Outcomes in Junior Orthopedic Residents.

Journal of visualized experiments : JoVE·2026
Same author

Retinal Pathology and Synucleinopathy in the Visual Pathway of α-Synuclein Preformed Fibril Mouse Model of Parkinson's Disease.

Brain and behavior·2026
Same author

Microbial community variations in human salivary samples with different body mass index for forensic research: a pilot study.

Frontiers in microbiology·2026
Same author

Numerical investigation of vanadium distill-condensation <i>via</i> computational fluid dynamics.

RSC advances·2026
Same author

Tribological behavior of laser-cladded CoCrFeNi system high-entropy alloy coatings before and after immersion in 3.5 wt. % NaCl solution.

iScience·2026
Same journal

Enhancing cereal productivity via nitrogen use efficiency: from conventional breeding to modern genomics.

Frontiers in genetics·2026
Same journal

Transcriptomic analysis reveals FcγR-mediated phagocytosis as a key pathway for the anti-inflammatory action of <i>Polygonatum sibiricum</i> polysaccharides in loach.

Frontiers in genetics·2026
Same journal

A novel <i>ABO</i> splice site variant underlying the A<sub>3</sub> phenotype: immunogenetic basis and functional dissection.

Frontiers in genetics·2026
Same journal

Case Report: Identification of two novel <i>ALMS1</i> variants in a patient with a ciliopathy resembling Alström syndrome.

Frontiers in genetics·2026
Same journal

Integrative analysis identifies Hspa5 as a key regulator of the ERS/UPR-immune axis in spinal cord injury.

Frontiers in genetics·2026
Same journal

Evaluation of genomic selection to improve survival of eastern oysters infected with <i>Perkinsus marinus</i>.

Frontiers in genetics·2026
See all related articles

Related Experiment Video

Updated: Jan 23, 2026

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.4K

LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation.

Le Ou-Yang1,2, Jiang Huang3, Xiao-Fei Zhang4

  • 1Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, China.

Frontiers in Genetics
|June 14, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method, TSSR, to predict links between long non-coding RNAs (lncRNAs) and diseases. TSSR accurately identifies potential lncRNA-disease associations, aiding disease understanding and treatment.

Keywords:
computational approachesdisease similaritylncRNA similaritylncRNAs-disease associations predictionsparse representation

More Related Videos

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Related Experiment Videos

Last Updated: Jan 23, 2026

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.4K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long non-coding RNAs (lncRNAs) play roles in biological processes.
  • lncRNA abnormalities are linked to complex diseases.
  • Identifying lncRNA-disease associations is crucial for disease understanding and treatment.

Purpose of the Study:

  • To propose a novel computational algorithm for predicting lncRNA-disease associations.
  • To address the challenge of precise prediction of potential lncRNA-disease associations.

Main Methods:

  • Developed a two-side sparse self-representation (TSSR) algorithm.
  • Learned self-representations of lncRNAs and diseases from known associations.
  • Utilized known associations and intra-associations from databases.

Main Results:

  • TSSR significantly outperformed competing methods on three real datasets.
  • Case studies on Melanoma, Glioblastoma, and Glioma demonstrated TSSR's effectiveness.
  • Identified candidate lncRNAs associated with these three cancers.

Conclusions:

  • TSSR is an effective computational approach for predicting lncRNA-disease associations.
  • The method aids in identifying potential biomarkers for complex diseases.
  • Further research can leverage TSSR for therapeutic target discovery.