This paper reports on the evaluation of different machine learning techniques for the automated classification of coding gene sequences obtained from several organisms in terms of their functional role as adhesins. Diverse, biologically-meaningful, sequence-based features were extracted from the sequences and used as inputs to the in silico prediction models. Another contribution of this work is the generation of potentially novel and testable predictions about the surface protein DGF-1 family in Trypanosoma cruzi. Finally, these techniques are potentially useful for the automated annotation of known adhesin-like proteins from the trans-sialidase surface protein family in T. cruzi, the etiological agent of Chagas disease.
|Number of pages||8|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|Publication status||Published - 2009|
- Adhesin-like proteins
- Chagas disease
- Genomic data mining
- Machine learning