Machine learning techniques for the automated classification of adhesin-like proteins in the human protozoan parasite Trypanosoma cruzi.
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.
González
Ana M
AM
Computer Science Department, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain. ana.marcos@uam.es
Azuaje
Francisco J
FJ
Ramírez
José L
JL
da Silveira
José F
JF
Dorronsoro
José R
JR
D000818:Animals; D001185:Artificial Intelligence; D014355:Chagas Disease / Q000469:parasitology; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006023:Glycoproteins / Q000737:chemistry; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D015233:Models, Statistical; D005810:Multigene Family; D009439:Neuraminidase / Q000737:chemistry; D040901:Proteomics / Q000379:methods; D015800:Protozoan Proteins / Q000737:chemistry; D014349:Trypanosoma cruzi / Q000145:classification / Q000235:genetics / Q000378:metabolism*
D006023:Glycoproteins; D008565:Membrane Proteins; D015800:Protozoan Proteins; C069715:trans-sialidase; D009439:Neuraminidase