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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 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,
Microbial Classification System01:24

Microbial Classification System

Classification is the process of organizing organisms into hierarchically inclusive groups based on their phenotypic similarities or evolutionary relationships. A species comprises one or more strains, and closely related species are grouped into genera. Genera are further classified into families, families into orders, orders into classes, and so forth, up to the domain level, which is the broadest taxonomic rank derived from a combination of phenotypic and genotypic data.The nomenclature of...
Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: May 7, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Alignment-free supervised classification of metagenomes by recursive SVM.

Hongfei Cui1, Xuegong Zhang

  • 1Department of Automation, Bioinformatics Division/Center for Synthetic & Systems Biology, TNLIST, MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China. zhangxg@tsinghua.edu.cn.

BMC Genomics
|September 24, 2013
PubMed
Summary

This study introduces a new alignment-free supervised classification method for metagenome analysis. The approach accurately distinguishes microbial communities, aiding in understanding human health conditions.

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Published on: February 15, 2017

Area of Science:

  • Bioinformatics
  • Microbial Ecology
  • Computational Biology

Background:

  • Metagenome sample comparison is crucial for studying microbial communities.
  • Alignment-free methods offer advantages over alignment-based approaches by not requiring genome annotations.
  • Existing alignment-free methods are unsupervised and cannot discriminate predefined classes.

Purpose of the Study:

  • To develop and evaluate an alignment-free supervised classification method for metagenome data.
  • To enable the discrimination of predefined classes within microbial communities.
  • To identify characteristic sequence features for class discrimination.

Main Methods:

  • Utilized k-tuple frequencies as features directly from metagenome short reads.
  • Employed Recursive Support Vector Machines (R-SVM) for feature selection and classification.
  • Tested the method on simulated, known genome, and human gut metagenome datasets.

Main Results:

  • The method achieved near-perfect classification on simulated data and identified key distinguishing sequence signatures.
  • Successfully discriminated between inflammatory bowel disease (IBD) patients and control samples using human gut metagenome data.
  • Outperformed unsupervised clustering approaches in discriminating specific sample classes.

Conclusions:

  • The proposed alignment-free supervised classification method effectively discriminates metagenomic samples into predefined classes.
  • The method successfully selects characteristic sequence features for discrimination.
  • Demonstrates the feasibility of using metagenome sequence features and supervised machine learning for studying human health conditions.