Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree.
We propose a new type of unsupervised, growing, self-organizing neural network that expands itself by following the taxonomic relationships that exist among the sequences being classified. The binary tree topology of this neutral network, contrary to other more classical neural network topologies, permits an efficient classification of sequences. The growing nature of this procedure allows to stop it at the desired taxonomic level without the necessity of waiting until a complete phylogenetic tree is produced. This novel approach presents a number of other interesting properties, such as a time for convergence which is, approximately, a lineal function of the number of sequences. Computer simulation and a real example show that the algorithm accurately finds the phylogenetic tree that relates the data. All this makes the neural network presented here an excellent tool for phylogenetic analysis of a large number of sequences.
Dopazo
J
J
Centro Nacional de Biotecnología, CSIC, Universidad Autónoma, Madrid, Spain.
Carazo
J M
JM
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D009696:Nucleic Acids / Q000145:classification* / Q000235:genetics; D010802:Phylogeny; D011506:Proteins / Q000145:classification* / Q000235:genetics; D016601:RNA-Binding Proteins / Q000145:classification; D012269:Ribosomal Proteins
D009696:Nucleic Acids; D011506:Proteins; D016601:RNA-Binding Proteins; D012269:Ribosomal Proteins; C067169:RPL22 protein, human