Soft Topographic Maps for Clustering and Classifying Bacteria Using Housekeeping Genes

المؤلفون المشاركون

Urso, Alfonso
La Rosa, Massimo
Rizzo, Riccardo

المصدر

Advances in Artificial Neural Systems

العدد

المجلد 2011، العدد 2011 (31 ديسمبر/كانون الأول 2011)، ص ص. 1-8، 8ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2011-10-12

دولة النشر

مصر

عدد الصفحات

8

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-485492