Unsupervised Statistical Learning of Context-free Grammar

Unold, Olgierd; Gabor, Mateusz; Wieczorek, Wojciech

doi:10.5220/0009383604310438

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Yet, it remains a goal for the future research to optimize learning, e.g. by enhancing the statistical learning with the contrastive estimation [89] whenever possible and combining with heuristic approaches [44, 90].…”

Section: Discussionmentioning

confidence: 99%

“…When applied to large generic set of rules constituting a covering grammar , the process of optimizing rule probabilities of which most eventually become zero, is akin to learning grammar. The most popular alternatives to IO are based on Genetic Algorithms (GA) using either a fixed set of rules [41, 29], as in the case of IO, or learnable set of rules [42, 43, 34, 44].…”

Section: Methodsmentioning

confidence: 99%

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Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

Dyrka

Gąsior-Głogowska

Szefczyk

2021

Preprint

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BackgroundAmyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite lack of apparent sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs.ResultsFirst, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy analyses of selected peptides to verify their structural and functional relationship.ConclusionsWhile the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

Dyrka

Gąsior-Głogowska

Szefczyk

2021

Preprint

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“…It is imperative to note that learning PCFG only from positive data leads to grammars, thereby making it difficult to discriminate negative sequences from the input data. To overcome these difficulties, we have recently proposed the novel algorithm for weighted CFG (WCFG) learning [5,6]. Weighted Grammar-based Classifier System (WGCS) is one of the few grammatical inference approaches learning both grammar structure (i.e., rules) and stochastic grammar parameters (i.e., weights of rules).…”

Section: Introductionmentioning

confidence: 99%

Split-Based Algorithm for Weighted Context-Free Grammar Induction

2021

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The split-based method in a weighted context-free grammar (WCFG) induction was formalised and verified on a comprehensive set of context-free languages. WCFG is learned using a novel grammatical inference method. The proposed method learns WCFG from both positive and negative samples, whereas the weights of rules are estimated using a novel Inside–Outside Contrastive Estimation algorithm. The results showed that our approach outperforms in terms of F1 scores of other state-of-the-art methods.

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