This vector is simply a one dimensional array of numbers which incorporate various features that the algorithm will use to determine protein-coding capacity. The measured vector is compared to an expected one obtained from a set of standard genes. In either case, an overall statistic is calculated and a decision made as to whether or not to present the model as a potential gene. Neural network methods are often used in which the algorithm is trained on a set of test genes and learns what weights should be assigned to the various measures in order give the best discrimination. They identified several measures which are particularly useful.
Methods differ in how the reference set is obtained and how the measure of fit is calculated.
Reference sets that incorporate information about the amino acid composition of the potential gene are superior to those that do not. Hexamers have proven useful. The presence of STOP codons. Most methods only consider ORFs. However, it is possible to incorporate stop codons into a measure of amino acid content.