Soft Topographic Maps for Clustering and Classifying Bacteria Using Housekeeping Genes

Joint Authors

Urso, Alfonso
La Rosa, Massimo
Rizzo, Riccardo

Source

Advances in Artificial Neural Systems

Issue

Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-8, 8 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2011-10-12

Country of Publication

Egypt

No. of Pages

8

Main Subjects

Information Technology and Computer Science

Abstract EN

The Self-Organizing Map (SOM) algorithm is widely used for building topographic maps of data represented in a vectorial space, but it does not operate with dissimilarity data.

Soft Topographic Map (STM) algorithm is an extension of SOM to arbitrary distance measures, and it creates a map using a set of units, organized in a rectangular lattice, defining data neighbourhood relationships.

In the last years, a new standard for identifying bacteria using genotypic information began to be developed.

In this new approach, phylogenetic relationships of bacteria could be determined by comparing a stable part of the bacteria genetic code, the so-called “housekeeping genes.” The goal of this work is to build a topographic representation of bacteria clusters, by means of self-organizing maps, starting from genotypic features regarding housekeeping genes.

American Psychological Association (APA)

La Rosa, Massimo& Rizzo, Riccardo& Urso, Alfonso. 2011. Soft Topographic Maps for Clustering and Classifying Bacteria Using Housekeeping Genes. Advances in Artificial Neural Systems،Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-485492

Modern Language Association (MLA)

La Rosa, Massimo…[et al.]. Soft Topographic Maps for Clustering and Classifying Bacteria Using Housekeeping Genes. Advances in Artificial Neural Systems No. 2011 (2011), pp.1-8.
https://search.emarefa.net/detail/BIM-485492

American Medical Association (AMA)

La Rosa, Massimo& Rizzo, Riccardo& Urso, Alfonso. Soft Topographic Maps for Clustering and Classifying Bacteria Using Housekeeping Genes. Advances in Artificial Neural Systems. 2011. Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-485492

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-485492