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Related Concept Videos

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:
Classification of Neurotransmitters01:30

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Enzymes02:34

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Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
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Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

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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,
Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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Updated: May 24, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A top-down approach to classify enzyme functional classes and sub-classes using random forest.

Chetan Kumar1, Alok Choudhary

  • 1Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60201, USA. chetankumar.iisc@gmail.com.

EURASIP Journal on Bioinformatics & Systems Biology
|March 2, 2012
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately distinguishes enzymes from non-enzymes and predicts enzyme function. This computational approach offers a faster, more reliable alternative to traditional experimental methods for enzyme classification.

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Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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Related Experiment Videos

Last Updated: May 24, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
08:10

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System

Published on: August 8, 2016

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Enzyme discovery is rapidly increasing due to advanced sequencing technologies.
  • Experimental enzyme function determination is time-consuming and expensive.
  • Existing computational methods struggle with enzymes of similar function but dissimilar sequences/structures.

Purpose of the Study:

  • Develop a computational method to classify enzyme sequences.
  • Distinguish enzymes from non-enzymes.
  • Predict enzyme function class and sub-class accurately.

Main Methods:

  • A supervised machine learning model was developed.
  • The model utilizes 73 sequence-derived features.
  • A three-layer random forest algorithm was employed for classification.

Main Results:

  • The model achieved high accuracy: 94.87% (enzyme vs. non-enzyme), 87.7% (function class), and 84.25% (sub-class).
  • Performance surpasses existing methods in many cases.
  • Feature selection identified biologically relevant attributes.

Conclusions:

  • The proposed machine learning model effectively predicts enzyme function.
  • This computational approach offers a significant advancement over traditional methods.
  • The model's accuracy and efficiency provide a valuable tool for enzyme research.