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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Gaussian Elimination: Problem Solving01:30

Gaussian Elimination: Problem Solving

Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...

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Related Experiment Video

Updated: Jun 27, 2026

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
15:07

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Published on: December 28, 2015

Functional assignment to JEV proteins using SVM.

Ganesh Chandra Sahoo1, Manas Ranjan Dikhit, Pradeep Das

  • 1BioMedical Informatics Division, Rajendra Memorial Research Institute of Medical Sciences, Agam Kuan, Patna-800007, India. ganeshiitkgp@gmail.com

Bioinformation
|December 5, 2008
PubMed
Summary

This study used support vector machines (SVM) to predict protein functions for Japanese encephalitis virus (JEV). Findings reveal diverse functions for JEV structural and nonstructural proteins, aiding in understanding infection and drug development.

Keywords:
Japanese encephalitis virus (JEV)SVMProtprotein function familysupport vector machine

Related Experiment Videos

Last Updated: Jun 27, 2026

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
15:07

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Published on: December 28, 2015

Area of Science:

  • Virology
  • Computational Biology
  • Protein Function Prediction

Background:

  • Understanding Japanese encephalitis virus (JEV) protein functions is crucial for mechanistic insights into infection.
  • Identifying protein functions can reveal novel targets for antiviral drug development.

Purpose of the Study:

  • To predict the functional classes of Japanese encephalitis virus (JEV) proteins using Support Vector Machines (SVM).
  • To elucidate the diverse roles of JEV structural and nonstructural proteins in the virus life cycle.

Main Methods:

  • Utilized SVMProt and available JEV sequences for protein function prediction.
  • Employed machine learning algorithms to classify protein functions based on sequence data.

Main Results:

  • JEV structural and nonstructural proteins exhibit a wide range of functions.
  • Common functions include iron-binding, metal-binding, lipid-binding, and transmembrane activity.
  • Nonstructural proteins are involved in actin binding, zinc-binding, and ATP-binding cassette (ABC) family functions.
  • Structural proteins are associated with nuclear receptor activity, RNA/DNA binding, and secretory pathways.

Conclusions:

  • The predicted diverse protein functions of JEV contribute to a deeper understanding of its life cycle.
  • These functional insights can guide the development of targeted therapeutic strategies against JEV infection.