Realistic three dimensional fitness landscapes generated by self organizing maps for the analysis of experimental HIV-1 evolution.

ABSTRACT: Human Immunodeficiency Virus type 1 (HIV-1) because of high mutation rates, large population sizes, and rapid replication, exhibits complex evolutionary strategies. For the analysis of evolutionary processes, the graphical representation of fitness landscapes provides a significant advantage. The experimental determination of viral fitness remains, in general, difficult and consequently most published fitness landscapes have been artificial, theoretical or estimated. Self-Organizing Maps (SOM) are a class of Artificial Neural Network (ANN) for the generation of topological ordered maps. Here, three-dimensional (3D) data driven fitness landscapes, derived from a collection of sequences from HIV-1 viruses after "in vitro" passages and labelled with the corresponding experimental fitness valu

SUBMITTER: Lorenzo-Redondo R 

PROVIDER: S-EPMC3938428 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature