A system for recognition of biological patterns in toxins using computational intelligence

A system for recognition of biological patterns in toxins using computational intelligence

This work presents an innovative way to find biological patterns in toxins in order to classify them according to their biological functions. Basing on relevant biological information (database) it was developed a system that uses computational intelligence to discover novel patterns within the prim...

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Bibliographic Details
Published in:2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology pp. 121 - 127
Main Authors: de Carvalho, B.P.R., Mendes, T.M., de Souza Ribeiro, R., Fortuna, R., Veneroso, J.M., de Alvarenga Mudado, M.
Format: Conference Proceeding
Language:English
Published: IEEE 01-03-2009
Subjects:
Amino acids
Calcium
Chemicals
Computational intelligence
Organisms
Pattern recognition
Peptides
Proteins
Sequences
Online Access:Get full text
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Summary:This work presents an innovative way to find biological patterns in toxins in order to classify them according to their biological functions. Basing on relevant biological information (database) it was developed a system that uses computational intelligence to discover novel patterns within the primary and secondary structures of a set of toxins. The discovered patterns make it possible to differentiate these toxins by their function: binding to specific channels for sodium, calcium or potassium ions. The classification rules are built using a given toxin database which is pre-processed according to the existence of signal peptide or propeptide in the primary sequence, together with the predicted secondary structures and its physical and chemical characteristics and water affinity information. The best obtained patterns are combined together in order to generate a final rule. All the experiments were performed using 802 toxin primary sequences labeled as channel functions obtained from two public databases, ATDB and Tox-Prot. After using the system to solve three different binary classification problems, each one for a specific ion channel, a committee is used to obtain the final classification label for each toxin. The committee got a classification accuracy of 80%, with correctness of 97%, 67% and 55% respectively to sodium, potassium and calcium channels.
ISBN:9781424427567
1424427568
DOI:10.1109/CIBCB.2009.4925717