Abstract
Grammatical inference (GI), i.e., the task of finding a rule that lies behind given words, can be used in the analyses of amyloidogenic sequence fragments, which are essential in studies of neurodegenerative diseases. In this paper, we developed a new method that generates non-circular parsing expression grammars (PEGs) and compares it with other GI algorithms on the sequences from a real dataset. The main contribution of this paper is a genetic programming-based algorithm for the induction of parsing expression grammars from a finite sample. The induction method has been tested on a real bioinformatics dataset and its classification performance has been compared to the achievements of existing grammatical inference methods. The evaluation of the generated PEG on an amyloidogenic dataset revealed its accuracy when predicting amyloid segments. We show that the new grammatical inference algorithm achieves the best ACC (Accuracy), AUC (Area under ROC curve), and MCC (Mathew’s correlation coefficient) scores in comparison to five other automata or grammar learning methods.
Highlights
The present work sits in the scientific field known as grammatical inference (GI), automata learning, grammar identification, or grammar induction [1,2]
We propose here using parsing expression grammars (PEGs), which are as fast as FSAs and can express more than context-free grammars (CFGs), in the sense that they can represent some context-sensitive grammars
We can read that a population size of P = 5 individuals were used for genetic programming (GP) runs along with others
Summary
The present work sits in the scientific field known as grammatical inference (GI), automata learning, grammar identification, or grammar induction [1,2]. Mathematicians investigate infinite sequences of words and for this purpose they proposed a few inference models. In the most popular model, Gold’s identification in the limit [3], learning happens incrementally. Very often in practice we deal only with a limited number of words (some of them being examples and others counter-examples). In such cases the best option is to use a selected heuristic algorithm, among which the most recognized instances include: evidence driven state merging [4], the k-tails method [5], the GIG method [6], the TBL (tabular representation learning) algorithm [7], the learning system ADIOS (automatic distillation of structure)
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