DC pole | Wartość | Język |
dc.contributor.author | Wieczorek, Wojciech | - |
dc.contributor.author | Unold, Olgierd | - |
dc.contributor.author | Strąk, Łukasz | - |
dc.date.accessioned | 2020-12-10T12:45:58Z | - |
dc.date.available | 2020-12-10T12:45:58Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | "Applied Science" (2020) iss. 23, art. no. 8747, s. 1-16 | pl_PL |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/17715 | - |
dc.description.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. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/ | * |
dc.subject | classification | pl_PL |
dc.subject | genetic programming | pl_PL |
dc.subject | grammatical inference | pl_PL |
dc.subject | parsing expression grammar | pl_PL |
dc.title | Parsing expression grammars and their induction algorithm | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.identifier.doi | 10.3390/app10238747 | - |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
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