Fuzzy Grammar-based Prediction of Amyloidogenic Regions

Olgierd Unold
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:210-219, 2012.

Abstract

In this paper, we address the problem of predicting the location of amyloidogenic regions in proteins. The language of protein sequence can be described by using a formal system such as fuzzy context-free grammar, and the problem of amyloidogenic region recognition can be replaced by fuzzy grammar induction. The induced fuzzy grammar achieved 70.6% accuracy and 96.7% specificity on a recently published amyloidogenic dataset. Our results are comparable to other methods dedicated to recognize amyloid proteins.

Cite this Paper

BibTeX
@InProceedings{pmlr-v21-unold12a, title = {Fuzzy Grammar-based Prediction of Amyloidogenic Regions}, author = {Unold, Olgierd}, booktitle = {Proceedings of the Eleventh International Conference on Grammatical Inference}, pages = {210--219}, year = {2012}, editor = {Heinz, Jeffrey and Higuera, Colin and Oates, Tim}, volume = {21}, series = {Proceedings of Machine Learning Research}, address = {University of Maryland, College Park, MD, USA}, month = {05--08 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v21/unold12a/unold12a.pdf}, url = {https://proceedings.mlr.press/v21/unold12a.html}, abstract = {In this paper, we address the problem of predicting the location of amyloidogenic regions in proteins. The language of protein sequence can be described by using a formal system such as fuzzy context-free grammar, and the problem of amyloidogenic region recognition can be replaced by fuzzy grammar induction. The induced fuzzy grammar achieved 70.6% accuracy and 96.7% specificity on a recently published amyloidogenic dataset. Our results are comparable to other methods dedicated to recognize amyloid proteins.} }
Endnote
%0 Conference Paper %T Fuzzy Grammar-based Prediction of Amyloidogenic Regions %A Olgierd Unold %B Proceedings of the Eleventh International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2012 %E Jeffrey Heinz %E Colin Higuera %E Tim Oates %F pmlr-v21-unold12a %I PMLR %P 210--219 %U https://proceedings.mlr.press/v21/unold12a.html %V 21 %X In this paper, we address the problem of predicting the location of amyloidogenic regions in proteins. The language of protein sequence can be described by using a formal system such as fuzzy context-free grammar, and the problem of amyloidogenic region recognition can be replaced by fuzzy grammar induction. The induced fuzzy grammar achieved 70.6% accuracy and 96.7% specificity on a recently published amyloidogenic dataset. Our results are comparable to other methods dedicated to recognize amyloid proteins.
RIS
TY - CPAPER TI - Fuzzy Grammar-based Prediction of Amyloidogenic Regions AU - Olgierd Unold BT - Proceedings of the Eleventh International Conference on Grammatical Inference DA - 2012/08/16 ED - Jeffrey Heinz ED - Colin Higuera ED - Tim Oates ID - pmlr-v21-unold12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 21 SP - 210 EP - 219 L1 - http://proceedings.mlr.press/v21/unold12a/unold12a.pdf UR - https://proceedings.mlr.press/v21/unold12a.html AB - In this paper, we address the problem of predicting the location of amyloidogenic regions in proteins. The language of protein sequence can be described by using a formal system such as fuzzy context-free grammar, and the problem of amyloidogenic region recognition can be replaced by fuzzy grammar induction. The induced fuzzy grammar achieved 70.6% accuracy and 96.7% specificity on a recently published amyloidogenic dataset. Our results are comparable to other methods dedicated to recognize amyloid proteins. ER -
APA
Unold, O.. (2012). Fuzzy Grammar-based Prediction of Amyloidogenic Regions. Proceedings of the Eleventh International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 21:210-219 Available from https://proceedings.mlr.press/v21/unold12a.html.

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